Joint Analysis of Stressors and Ecosystem Services to Enhance Restoration Effectiveness

Article (PDF Available)inProceedings of the National Academy of Sciences 110(1) · December 2012with 283 Reads
DOI: 10.1073/pnas.1213841110 · Source: PubMed
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Abstract
With increasing pressure placed on natural systems by growing human populations, both scientists and resource managers need a better understanding of the relationships between cumulative stress from human activities and valued ecosystem services. Societies often seek to mitigate threats to these services through large-scale, costly restoration projects, such as the over one billion dollar Great Lakes Restoration Initiative currently underway. To help inform these efforts, we merged high-resolution spatial analyses of environmental stressors with mapping of ecosystem services for all five Great Lakes. Cumulative ecosystem stress is highest in near-shore habitats, but also extends offshore in Lakes Erie, Ontario, and Michigan. Variation in cumulative stress is driven largely by spatial concordance among multiple stressors, indicating the importance of considering all stressors when planning restoration activities. In addition, highly stressed areas reflect numerous different combinations of stressors rather than a single suite of problems, suggesting that a detailed understanding of the stressors needing alleviation could improve restoration planning. We also find that many important areas for fisheries and recreation are subject to high stress, indicating that ecosystem degradation could be threatening key services. Current restoration efforts have targeted high-stress sites almost exclusively, but generally without knowledge of the full range of stressors affecting these locations or differences among sites in service provisioning. Our results demonstrate that joint spatial analysis of stressors and ecosystem services can provide a critical foundation for maximizing social and ecological benefits from restoration investments.
Joint analysis of stressors and ecosystem services
to enhance restoration effectiveness
J. David Allan
a,1,2
, Peter B. McIntyre
b,1
, Sigrid D. P. Smith
a,1
, Benjamin S. Halpern
c
, Gregory L. Boyer
d
,
Andy Buchsbaum
e
, G. A. Burton, Jr.
a,f
, Linda M. Campbell
g
, W. Lindsay Chadderton
h
, Jan J. H. Ciborowski
i
,
Patrick J. Doran
j
, Tim Eder
k
, Dana M. Infante
l
, Lucinda B. Johnson
m
, Christine A. Joseph
a
, Adrienne L. Marino
a
,
Alexander Prusevich
n
, Jennifer G. Read
o
, Joan B. Rose
l
, Edward S. Rutherford
p
, Scott P. Sowa
j
, and Alan D. Steinman
q
a
School of Natural Resources and Environment and
f
Cooperative Institute of Limnology and Ecosystems Research, University of Michigan, Ann Arbor, MI
48109;
b
Center for Limnology, University of Wisconsin, Madison, WI 53706;
c
National Center for Ecological Analysis and Synthesis, and Center for Marine
Assessment and Planning, University of California, Santa Barbara, CA 93101;
d
Great Lakes Research Consortium and College of Environmental Science and
Forestry, State University of New York, Syracuse, NY 13210;
e
Great Lakes Regional Center, National Wildlife Federation, Ann Arbor, MI 48104;
g
Environmental
Science, St. Marys University, Halifax, NS, Canada B3H 3C3;
h
The Nature Conservancy Great Lakes Project, care of the Notre Dame Environmental Change
Initiative, South Bend, IN 46617;
i
Department of Biological Sciences, University of Windsor, Windsor, ON, Canada N9B 3P4;
j
The Nature Conservancy Great
Lakes Project, Lansing, MI 48906;
k
Great Lakes Commission, Ann Arbor, MI 48104;
l
Department of Fisheries and Wildlife, Michigan State University, East
Lansing, MI 48824;
m
Natural Resources Research Institute, University of Minnesota, Duluth, MN 55811;
n
Earth Systems Research Center, University of New
Hampshire, Durham, NH 03824;
o
Michigan Sea Grant and Great Lakes Observing System, Ann Arbor, MI 48104;
p
Great Lakes Environmental Research
Laboratory, National Oceanic and Atmospheric Administration, Ann Arbor, MI 48108; and
q
Annis Water Resources Institute, Grand Valley State University,
Muskegon, MI 49441
Edited by Peter M. Kareiva, The Nature Conservancy, Seattle, WA, and approved October 31, 2012 (received for review August 13, 2012)
With increasing pressure placed on natural systems by growing
human populations, both scientists and resource managers need
a better understanding of the relationships between cumulative
stress from human activities and valued ecosystem services. Socie-
ties often seek to mitigate threats to these services through large-
scale, costly restoration projects, such as the over one billion dollar
Great Lakes Restoration Initiative currently underway. To help
inform these efforts, we merged high-resolution spatial analyses of
environmental stressors with mapping of ecosystem services for all
ve Great Lakes. Cumulative ecosystem stress is highest in near-
shore habitats, but also extends offshore in Lakes Erie, Ontario, and
Michigan. Variation in cumulative stress is driven largely by spatial
concordance among multiple stressors, indicating the importance
of considering all stressors when planning restoration activities. In
addition, highly stressed areas reect numerous different combina-
tions of stressors rather than a single suite of problems, suggesting
that a detailed understanding of the stressors needing alleviation
could improve restoration planning. We also nd that many impor-
tant areas for sheries and recreation are subject to high stress,
indicating that ecosystem degradation could be threatening key
services. Current restoration efforts have targeted high-stress sites
almost exclusively, but generally without knowledge of the full
range of stressors affecting these locations or differences among
sites in service provisioning. Our results demonstrate that joint
spatial analysis of stressors and ecosystem services can provide
a critical foundation for maximizing social and ecological benets
from restoration investments.
Laurentian Great Lakes
|
cumulative impact
|
marine spatial planning
|
fresh water
T
he Laurentian Great Lakes contain over 80% of North Amer-
icas surface fresh water and are a critical resource to commu-
nities throughout the region (1). Lake-dependent commerce in US
counties bordering the Lakes provided 1.5 million jobs generating
US$62 billion in wages in 2010 (2). Economic activity associated
with recreational shing is estimated to be at least $7 billion annually
(3), and millions of visitors swim, boat, and watch wildlife along the
Lakes each year. Despite clear societal dependence on the Great
Lakes, their condition continues to be degraded by numerous en-
vironmental stressors likely to have adverse impacts on species and
ecosystems (4). As a result, water-quality advisories and beach
closings are frequent occurrences, embodying both the human and
natural costs of declines in ecosystem health (5).
Managing and restoring these high-value ecosystems has often
been piecemeal, emphasizing one or a few stressors that garner
public attention (e.g., an invasive species, nutrient run-off), or
focusing on mitigation specic to a particular ecosystem service
(e.g., sheries management, recreational access) (e.g., ref. 6).
Recent studies have demonstrated the value of more compre-
hensive assessments for prioritizing restoration investments,
particularly when a broad suite of stressors or services can be
quantied and mapped (710). However, to date the overlap and
interaction between the cumulative impact of stressors and ser-
vice provisioning has not been assessed in any ecosystem.
Restoration efforts explicitly merge concerns about stressors
and services by seeking to reduce human impacts to increase
provisioning of services. Since 2009, the Great Lakes have been
the focus of a major restoration initiative entailing proposed
expenditures of greater than $1 billion over 5 y by the US gov-
ernment (4), targeting invasive species, nonpoint run-off, chemical
pollution, and habitat alteration. High return on this restoration
investment is expected because of enhanced property values, re-
duced water treatment costs, and increased tourism, recreation,
and sheries (11). The current initiative specically targets key
classes of environmental stressors that were identied through a
planning process involving numerous government agencies and
environmental groups. However, despite the fact that both stres-
sors and services occur in dened locations and vary greatly across
space in magnitude, no comprehensive spatial analysis has been
available to guide restoration efforts in the Great Lakes.
Quantifying and mapping the separate and cumulative inuence
of diverse stressors is an emerging new approach for optimizing
restoration investments (7, 8, 12). The lack of comprehensive,
spatially explicit stressor analyses raises at least three concerns.
First, optimal targeting of restoration efforts often will require ac-
counting for a wide range of stressors that differ in relative impact.
Second, major investments in remediating a subset of stressors at
Author contributions: J.D.A., P.B.M., and B.S.H. designed research; J.D.A., P.B.M., S.D.P.S.,
B.S.H., G.L.B., A.B., G.A.B., L.M.C., W.L.C., J.J.H.C., P.J.D., T.E., D.M.I., L.B.J., C.A.J., A.L.M.,
A.P., J.G.R., J.B.R., E.S.R., S.P.S., and A.D.S. performed research; J.D.A., P.B.M., S.D.P.S., and
B.S.H. analyzed data; and J.D.A., P.B.M., S.D.P.S., and B.S.H. wrote the paper.
The authors declare no conict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
Data deposition: The data reported in this paper are available at www.greatlakesmapping.org.
1
J.D.A., P.B.M., and S.D.P.S. contributed equally to this work.
2
To whom correspondence should be addressed. E-mail: dallan@umich.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1213841110/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1213841110 PNAS Early Edition
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a site may have little net benet if other stressors remain un-
addressed. Finally, restoration planning is increasingly oriented
toward maintaining or enhancing ecosystem services (13, 14),
which requires identifying locations where actual or potential
provision of services is greatest. Thus, understanding the spatial
distributions of both stressors and ecosystem services can greatly
enhance the strategic targeting of restoration efforts. Here we
present a high-resolution assessment of cumulative stress (here-
after abbreviated CS) across the Great Lakes based on 34 stres-
sors, ranging from shing to land-based pollution to climate
change (SI Text, Tables S1 and S2). These individual stressors
represent all major classes of stressors in the region, and were
weighted to reect their relative impact on ecosystem condition.
We then compare patterns of CS with the spatial distribution of
seven ecosystem services related to food provisioning and recre-
ational activities. Our results illustrate how joint analysis of
stressors and services can be an important step toward maximizing
social and ecological benets from restoration investments.
Results and Discussion
Cumulative Stress Analysis. Our CS index highlights major spatial
disparities in human inuence across the Great Lakes (Fig. 1).
Large subregions of moderate to high CS are apparent in Lakes
Erie and Ontario, Saginaw and Green Bays, and along Lake
Michigans shoreline (Fig. 1). In contrast, extensive offshore
areas of Lakes Superior and Huron, where the coasts are less
populated and developed, experience relatively low stress (Fig.
2A). Although the median value of CS across the Lakes is 0.14
and <10% of pixels score above 0.3 (Fig. S1), most areas expe-
rience 1015 stressors with nonzero levels (mean = 12.9 ± 2.6
SD, minimum = 8). Thus, a focus on one or a few stressors will
miss the majority of the stressors affecting any given location. CS
also differs strongly among habitats. The highest stress is seen in
wetlands and river mouths, and CS declines rapidly from the
shoreline to offshore (Fig. 2B). Near-shore habitats generally
experience 1218 stressors (mean = 15.2 ± 3.0 SD, maximum =
31), reecting the coincidence of land- and lake-based stressors.
This pattern is troubling from a biodiversity perspective, because
roughly 90% of Great Lakes sh and invertebrate species occupy
near-shore habitats (15).
Variation in CS is driven largely by concordant spatial patterns
in multiple stressors, although few stressors are strongly corre-
lated. Individual stressor intensities show broad positive corre-
lations with CS across the Great Lakes, with the exception of
copper contamination and climate-driven water warming (Fig.
3A). High CS results from above-average values of many different
Fig. 1. The spatial pattern of CS from 34 human-induced stressors across the Laurentian Great Lakes and in selected regions. Cumulative stress was calculated
based on the intensities of each stressor weighted by their impact (determined from expert judgment). We show CS on a relative (percentile) scale, grouped
by quintiles; pixels representing the highest 20% CS are red, and the lowest 20% are dark blue. See Fig. S1 for the CS ranges of these quintiles.
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classes of cooccurring stressors (Fig. 3B) rather than extreme
values of any single stressor. Therefore, restoration efforts aimed
at mitigating one or a few types of threats could fail to improve
ecosystem conditions because of ongoing degradation from re-
maining stressors. Ideally, restoration planning should explicitly
address multiple stressors and design interventions based on the
relative impact of each stressor present at a site. Furthermore,
high CS does not arise from a consistent suite of stressors. In-
stead, the lack of clustering of high-CS pixels in multivariate
analyses of stressor intensities indicates that high stress results
from a wide range of stressor combinations, although modest
differences among lakes are evident (Fig. 3C). Sensitivity analyses
show that spatial patterns of CS are robust to alternative stressor
weights, normalization methods, and elimination of any particu-
lar stressor at both local and whole-basin scales (SI Text).
Interestingly, the spatial distribution of current restoration
investments is focused almost entirely on high-stress locations.
Among 33 long-standing areas of concern (AOCs), which are
often associated with polluted rivers (16), and 231 georeferenced
projects under the US Great Lakes Restoration Initiative
(GLRI) (4), most are in the highest quintile of CS (Fig. 4 A and
B and Fig. S2). This pattern presumably reects the spatial
correlation of most individual stressors with CS, including the
stressors for which remediation is a priority under the AOC and
GLRI programs. Although a focus on one or a few stressors may
identify important locations to target, use of a more compre-
hensive, multistressor approach increases the likelihood that
mitigation efforts will address all important stressors at a site.
Overlap of Ecosystem Services and CS. Comparing the spatial dis-
tributions of CS and ecosystem services reveals that locations
supporting Great Lakes sheries and recreation are dispropor-
tionately stressed (Fig. 4C). In particular, the locations of bea-
ches, marinas, and perch spawning areas are strongly skewed
toward high-CS areas. These patterns reect broad north-south
gradients in lake productivity and human population densities,
both of which peak in Lakes Erie, Ontario, and southern Lake
Michigan. Furthermore, high CS at bird-watching and charter
shing sites results from the concentration of human impacts
along the shoreline and in wetlands and river mouths. In con-
trast, the skew in CS is lower for commercial shing, which is
widely distributed throughout all lakes, and lake trout spawning,
which is concentrated in Lakes Michigan, Huron, and Superior,
where average CS values are relatively low.
Interpretation of the spatial coincidence of CS and ecosystem
services (Fig. 4C) depends on two assumptions: whether our
multistressor index is an appropriate measure of stress to each
service, and whether all service locations actually deliver benets
to people (i.e., have service value). We recognize that not all
stressors affect a given service equally, so to test the rst as-
sumption we identied a subset of stressors expected to most
directly and strongly inuence each service. For example, we
identied three stressors strongly affecting birding (light pollu-
tion, road density, and coastal development), and 10 stressors
that have strong effects on commercial and recreational sh-
ing (Table S3). Consistent with our analysis based on the full CS
(Fig. 4C), services occur disproportionately in locations where
the most relevant subset of stressors indicates high stress levels
(Fig. 4D). As before, lake trout spawning and commercial shing
show the least departure from the null case where service loca-
tions are randomly distributed with respect to CS. For all
services, departures from the null pattern are somewhat less
pronounced when considering only the most relevant stressors,
implying that mitigating a modest number of key stressors could
result in measureable improvements in benets.
For several services, including birding, beaches, and the two
sh-spawning datasets, we did not have information on actual
delivery of the service. Birding sites are a small subset of high-
value sites identied by experts, or featured in birding festivals,
so the assumption that they are visited seems reasonable. Beach
visitation data are not available, but aerial views of beaches that
had the fewest people living within a 30-km radius revealed
campgrounds and road access for most, indicating that few if any
beaches are unvisited. Spawning locations are compiled from
historical data but are not individually monitored, so we must
assume that all of them contribute similarly to the recruitment of
these important shery species.
In locations of high stress and low service provisioning, further
investigations will be needed to ascertain whether services have
always been low, or instead are currently suppressed by stressors.
Only in the latter case is restoration likely to lead to improve-
ments. Similarly, the cooccurrence of many service locations with
high stress (Fig. 4 C and D) requires further research to de-
termine if these services would benet from restoration or are
sufciently resilient to stress that restoration is unnecessary.
However, beach closings (17), sport shery declines (18), and
other types of foregone recreational opportunities suggest that
stressor mitigation could indeed enhance service provisioning.
For example, a number of studies have found that improvements
in water quality result in increased benets (19), consistent with
estimates that Great Lakes restoration efforts could yield returns
in excess of $50 billion beyond their costs (11). Although
0.6
0.8
1.0
tive stress
AB
LS LM LH LE LO
0.0
0.2
0.4
WR L-H L-S S-H S-S Off
ta
t
ib
aH
e
ka
L
Cumula
Fig. 2. Boxplots of cumulative stress for each lake (A) and habitat (B) in the Laurentian Great Lakes, showing medians and quartiles as boxes, 1.5× inter-
quartile range as whiskers, and outliers as circles. Abbreviations used: Lakes Superior (LS), Michigan (LM), Huron (LH), Erie (LE), Ontario (LO); wetlands and
river mouths (WR), littoral-hard substrate (L-H), littoral-soft substrate (L-S), sublittoral-hard substrate (S-H), sublittoral-soft substrate (S-S), offshore (Off).
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uncertainty remains about how decreases or increases in CS will
translate into changes in particular services, there is reason for
optimism that reducing ecosystem stress may provide tangible
benets to the region.
Restoration Opportunities
Our analysis highlights the potential to broaden the current port-
folio of restoration projects by identifying locations of moderate-
to-high CS that are not currently targeted for restoration, as well as
sites not currently highly stressed that would benet from miti-
gation of particular stressors. Particularly compelling opportuni-
ties arise when ecosystem services are high at sites where few
stressors must be alleviated to signicantly lower CS. For example,
although most of Lake Ontario is in the highest quintile of CS,
both the number of stressors to be alleviated (e.g., Fig. 3B)and
levels of valued services vary widely among sites. The northeastern
end of Lake Ontario exemplies the opportunity to address mul-
tiple services by mitigating fewer stressors. At the other end of the
CS spectrum, our approach enables identication of low-CS sites
where services are high. These places may also offer high return
on restoration investment because relatively few issues must be
addressed and much service value could be lost if their CS levels
were to increase.
Joint analysis of CS and ecosystem services also suggests that
return on restoration investments may be low when high-CS sites
require remediation of many stressors yet currently provide few
services. Although our analysis focused on the limited set of
services for which spatial data are available, it uncovered a number
of current restoration project sites with high CS but low service
provisioning. These locations would not be identied as high pri-
orities based on a full analysis of stressors and services, although
they may offer other benets for which we have not accounted.
Indeed, we advocate expanding the approach developed here to
encompass additional value frameworks, such as protecting un-
developed areas or species and habitats of concern, and we rec-
ognize that restoration decisions must account for a variety of
other factors such as economic costs, public perception, and eq-
uitable distribution of funding opportunities as well. Nevertheless,
spatial analysis of both CS and ecosystem services provides a fresh
perspective on prioritizing restoration sites and actions. Explicitly
accounting for ecosystem services may also enhance the willingness
of the public and policy-makers to support restoration efforts.
Conclusions
Given the large number of individual stressors included and the
robustness of our results in sensitivity analyses (Table S1, Fig. S3),
A
B
C
Fig. 3. Relationship between CS and individual stressor intensities in the Laurentian Great Lakes. (A) The correlation coefcient for each individual stressor
map with the CS map, plotted as bars for better visualization. Because most stressors are positively correlated with CS (A), the number of stressors above their
basin-wide average in each pixel (B) contributes strongly to variation in CS. However, unconstrained ordination of stressors in high-stress (CS > 0.8) pixels (C)
failed to identify a consistent suite of operative stressors. The PCA biplot (C) shows factor loadings of stressors as arrows and site scores as points colored by
lake (n = 47,899 pixels). See SI Text for descriptions of each stressor; lake abbreviations as in Fig. 2. CSOs, combined sewer overows.
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the patterns of ecosystem degradation revealed by our CS index
across the 244,000 km
2
of Great Lakes waters are unlikely to
change with additional information. Nonetheless, interpretation of
our results must recognize several limitations. We used a 1-km
2
grid to resolve shoreline features, but variation in the native scale
of data and assumptions of stressor decay with distance from input
sources make our results most useful for identifying broad-scale
patterns. The spatial distributions of some important stressors
could not be quantied, including additional invasive and nuisance
species, recreational shing, sh diseases, and emerging toxic
chemicals. Our CS index is additive because interactions among
stressors (20, 21) and nonlinear impacts on ecosystems are poorly
understood. For example, apex predators in Lake Huron have
collapsed following dreissenid mussel invasion (22), but this syn-
ergy cannot yet be predicted. Future assessments of ecosystem
services would benet from comparative valuation data and from
direct evidence of service response to stressor mitigation, both of
which are major gaps in current understanding of the Great
Lakes and other ecosystems. Finally, economic costs and politi-
cal constraints strongly inuence real-world restoration decisions
(12, 14), but are beyond the scope of our analysis.
Enormous societal investments in restoration of the Great
Lakes and other critical ecosystems are underway, providing
high-prole tests of our ability to improve ecosystem conditions
and human well-being. Prioritizing on-the-ground actions within
these efforts is challenging when dozens of stressors are in play
and their relative importance varies in space. High-resolution
spatial analysis is an effective approach for assessing human
impact on ecosystems at global (7, 8) to regional (23) scales, and
can assist restoration efforts by identifying the full range of
stressors that degrade ecosystem condition at any given site.
Here, we extend this approach to account for ecosystem services
and place current restoration efforts in a multistressor context.
Our results show that additional restoration investments in the
Great Lakes are warranted, and provide a means of targeting
them at the stressors and sites where societal and ecological
benets would be maximized.
Materials and Methods
We assembled data for 34 stressors likely to have adverse impacts on species,
biological communities, or ecosystem dynamics across the entire surface of
the Great Lakes, excluding connecting channels (SI Text). Stressors were
mapped at a 1-km
2
resolution to adequately represent shoreline and
bathymetric features of the lakes. Datasets used to generate individual maps
differed in their native resolution (Table S2), and we used standard geo-
spatial methods for resampling and interpolation to convert them to a
common grid (SI Text). When original dataset extents did not align with our
template because of boundary inconsistencies, small gaps with no data
values near the shoreline were lled in by interpolation.
We modeled the spatial footprint of stressors with inuence beyond their
point of origin (e.g., sediment loads entering a lake from a river) in two ways
(SI Text). For stressors from tributary inputs, we modeled dispersal over
distance from the river mouth into the lake using an exponential decay
function with stressor-specic coefcients. For shore-based stressors, we
assumed that inuence extended 1 km into the lake and transferred the
shore-side stressor value to the adjacent lake-side pixel. Although stressor
decay estimates are uncertain, we have used reasonable estimates based on
the literature and consultations with subject-area experts. To account for
the differential vulnerability of various habitats to each threat, we de-
veloped a habitat classication based on bathymetry, substrate composition,
and the locations of wetlands and river mouths (Fig. S4). We combined
wetlands and river mouths because many important wetlands within the
Great Lakes are associated with river mouths and to simplify the number of
categories needed for an expert survey. Using expert elicitation methods
A
25
200
B
10
15
20
100
150
mber of sites
mber of sites
0.0 0.2 0.4 0.6 0.8 1.0
0
5
0.0 0.2 0.4 0.6 0.8 1.0
0
50
Cumulave
stress
Nu
Cumulave stress
Nu
DC
sites
sites
0.8
1.0
Commercial fishing
Spawning (trout)
Beaches
Charter fishing
0.8
1.0
enumberof
enumberof
0.4
0.6
Marinas
Spawning (perch)
Birding
0.4
0.6
Cumulav
Cumulave stress
Cumulav
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
Cumulave
stress
Fig. 4. Locations of current restoration efforts and valued ecosystem services coincide with areas of high CS in the Laurentian Great Lakes. Histograms of the
frequency of CS at 33 AOC (A) and 231 GLRI sites (B) show that these sites are predominantly in locations with high CS. (C) The cumulative frequency of CS in
locations of seven ecosystem services (sample sizes: beaches, 1,265; marinas, 445; birding, 297; charter shing, 240; lake trout spawning, 1,143; yellow perch
spawning, 336). Each curve shows the proportion of sites at or below a given CS. All curves fall below the 1:1 line, indicating that these services occur in areas
of higher CS than expected at random. (D) The cumulative frequency of stress in locations of the same seven ecosystem services, where stress is estimated
using the most relevant subset of stressors specic to each service.
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Supplementary resources

  • Article
    We describe development anthropogenic stress indices for coastal margins of the Laurentian Great Lakes basin. Indices were derived based on the response of species assemblages to watershed-scale stress from agriculture and urbanization. Metrics were calculated for five groups of wetland biota: diatoms, wetland vegetation, aquatic invertebrates, fishes, and birds. Previously published community change points of these assemblages were used to classify each watershed as ‘least-disturbed’, ‘at-risk’, or ‘degraded’ based on community response to these stressors. The end products of this work are an on-line map utility and downloadable data that characterize the degree of agricultural land use and development in all watersheds of the US and Canadian Great Lakes basin. Discrepancies between the observed biological condition and putative anthropogenic stress can be used to determine if a site is more degraded than predicted based on watershed characteristics, or if remediation efforts are having beneficial impacts on site condition. This study provides a landscape-scale evaluation of wetland condition that is a critical first step for multi-scale assessments to help prioritize conservation or restoration efforts.
  • Thesis
    Full-text available
    Wilderness areas hold an exceptional range of environmental values but are being rapidly destroyed. In this thesis I addressed key questions relevant to conserving wilderness and biodiversity. I created the first temporally intercomparable global maps of terrestrial wilderness, enabling an analysis of recent wilderness loss. I then analysed where wilderness loss is impacting threatened species globally, and Natural World Heritage Sites, which are important places set aside to protect threatened species and wilderness. I then analysed opportunities for the World Heritage Convention to contribution to wilderness conservation, and present a case study of wilderness conservation in an African protected area.
  • Chapter
    Full-text available
    4.1 EXECUTIVE SUMMARY 1 The most important indirect anthropogenic drivers of changes in nature, nature’s contributions to people and good quality of life include unsustainable patterns of economic growth (including issues related to international trade and finances); population and demographic trends; weaknesses in the governance systems and inequity (well established). Increasing human demand for food, water, and energy caused by increases in population, per capita Gross Domestic Product and international trade have had negative consequences for nature and many regulating and non-material nature’s contributions to people. 2 Social inequity is a concern with adverse implications for nature, nature’s contributions to people and good quality of life (well established). When the United Nations Development Program Human Development Index is adjusted for inequality, it is 22 per cent lower in Latin American and Caribbean countries and 11.1 per cent lower in North America {4.3.6}. Seventy-two million people escaped income-poverty from 2003-2013 in Latin America; however, around 26.9 per cent of the Latin American population still lived in poverty in 2012: 40.6 per cent in Mesoamerica and 21 per cent in South America {4.3.6}. In many cases, poor people in the Americas tend to increase the pressures on nature merely to survive, while on the other hand, there is high per capita consumption of natural resources in affluent segments of the population. 3 Economic growth (measured as Gross Domestic Product growth and Gross Domestic Product per capita) and international trade are major drivers of natural resource consumption in the Americas. Economic growth and trade can positively or negatively impact biodiversity and nature’s contributions to people, but currently, on balance, they adversely impact biodiversity and nature’s contributions to people when environmental and social development goals are insufficiently accounted for (well established). Positive impacts of economic growth and international trade may include a stronger economy and increased employment, and social and environmental investments such as biodiversity protection. Negative impacts of economic growth include unsustainable conversion, use and exploitation of terrestrial, freshwater and marine ecosystems and resources, which threaten biodiversity and degrade nature’s contributions to people by reducing species abundances below self-sustaining levels and by disrupting key ecosystem functions {4.6}. The Americas generates around 18 per cent of world exports, with 70 per cent of this from North America. The Latin American and Caribbean contributions to world exports is 5.4 per cent, and natural resource governance is strongly influenced by having economies dominated by commodity exports. Natural resources (oil, minerals, and agriculture) contribute more than 50 per cent to these Latin America and the Caribbean exports {4.3.3}. Globalization has catalyzed rapid growth of international trade and become an important motor for regional development, but it has also disconnected places of production, transformation and consumption of land-based products. This decoupling places significant challenges for socio-environmental governance and regulatory implementation for sectors rapidly changing in response to increases in the global demand for food, feed and fiber. Consequently, natural resource use policies often come into place only after fundamental shifts in the land-use system are already underway, and interventions have become costly and have limited influence {4.6}. 4 Weaknesses in the governance systems and institutional frameworks in the Americas have had adverse implications for nature, nature’s contributions to people and good quality of life in the Americas (well established). In most countries in the region centralized modes of governance still prevail where decision-making regarding Nature and nature’s contributions to people in reality falls on the State. Centralized command and control measures nonetheless, such as the establishment of protected areas, continue to be a pillar of biodiversity conservation. Significant progress has been made to include other actors and new hybrid governance modes such as public-private certification CHAPTER 4. DIRECT AND INDIRECT DRIVERS OF CHANGE IN BIODIVERSITY AND NATURE’S CONTRIBUTIONS TO PEOPLE 299 schemes or payment for ecosystem services, which are in line with the rising role of markets in environmental governance. These transformations from centralized to descentralized forms, however, have led to significant socioenvironmental conflicts in the region {4.3.1}. 5 Value systems in the Americas differ among cultural groups and identities across the whole region and shape governance systems, in particular the ways of addressing development policies, land tenure and indigenous rights, and strongly influence decisions on land use and natural resources exploitation in the different subregions (well established). Indigenous and traditional peoples throughout the Americas have developed many different socio-economic systems (nationally and locally). Indigenous and local knowledge are expressions of social articulations that can positively influence biodiversity and ecosystem services. While cases that conservation of biodiversity and nature’s benefits to people are related to empowerment of indigenous and traditional communities are emerging in the region (for example, the role of indigenous land on deforestation control in tropical forests of South America), weak and less participatory governance systems are associated with cases of conflicts in managing land and natural resources in all of the Americas subregions (for example, conflicts related to infrastructure building in indigenous lands) {4.3.1, 4.3.6}. 6 Habitat conversion, fragmentation and overexploitation/overharvesting are resulting in a loss of biodiversity and a loss of nature’s contributions to people in all ecosystems. Habitat degradation due to land conversion and agricultural intensification; wetland drainage and conversion; urbanization and other new infrastructure, and resource extraction is the largest threat to fresh water, marine and terrestrial biodiversity and nature’s contributions to people in the Americas (well established). The resulting changes in terrestrial, freshwater and marine environments are interrelated and often lead to changes in biogeochemical cycles, pollution of ecosystems and eutrophication, and biological invasions, which are at the same time significant direct drivers of change in the region (well established). The expansion and intensification of agriculture and livestock production in the Americas are decreasing the area of and altering natural ecosystems (well established) {4.4.1}. Related changes include shifting drainage patterns (affecting infiltration and runoff), water quality degradation, soil disturbance, habitat loss, and release of chemicals that can be toxic to biota and human populations. Nitrogen and phosphorus fertilizer use have greatly contributed to increases in the amount of available nitrogen and phosphorus in the environment, doubling available nitrogen, for example, with negative consequences for ecosystem function, and air, soil and water quality {4.4.2}, including major contributions to coastal and freshwater oxygen depletion. Land-use changes, road and trail construction, waterways and domestic animals are common dispersal routes for invasive species (well established) {4.4.4}. Habitat conversion also decreases connectivity among, and diversity within, remaining fragments of natural ecosystems (well established). Wildlife, fisheries, and people, including many indigenous peoples, are exposed to residual pollution in the environment. Mining for trace metal ores and coal has left lasting legacies of toxic pollution across the region {4.4.2} (well established). Although unsustainable management of natural resources are threatening biodiversity and degrading nature’s contributions to people by reducing populations below natural self-sustaining levels and disrupting ecosystem functions {4.4.5}, some sustainable practices have been identified and used in terrestrial and aquatic environments. 7 Rapid urbanization is a key driver of loss of biodiversity and nature’s contributions to people, but the nature and the magnitude of impacts vary substantially among subregions of the Americas (established but incomplete). The Americas region is highly urbanized, with about 80 per cent of the region’s population residing in urban settings {4.3.5}. Although urban population impacts depend on consumption patterns and lifestyles, which vary considerably from one subregion to another, in all subregions a large number of ecosystems have been affected. Urbanization driven by growing populations and internal migration acts as an indirect driver of land-use change through linear infrastructures. In Latin America and the Caribbean, 12 per cent of the urban population and 36 per cent of rural population do not have access to improved sanitation facilities, and only 50 per cent of the population in Latin America is connected to sewerage. The poor systematic waste management in Latin America and the Caribbean implies pollution of inland waters and coastal areas {4.4.2} affecting biodiversity and human health. 8 Carbon dioxide emissions from fossil fuel production continue to increase, increasing 29 per cent from 2000 to 2008. The combustion of fossil fuels is not only the primary source of anthropogenic greenhouse gases that cause human-induced climate change, but fossil fuel combustion itself is also a major source of pollution adversely impacting most terrestrial and marine ecosystems and human health {4.4.2} (well established). Air pollution (especially particulates, ozone, mercury, and carcinogens) causes significant adverse health effects on infants, adults and biodiversity (well established), and carbon dioxide emissions cause ocean acidification. For example, the combustion of fossil fuels account for 25 per cent of the direct anthropogenic mercury emissions that are increasing the mercury burden of polar and subpolar wildlife and indigenous people with diets dominated THE REGIONAL ASSESSMENT REPORT ON BIODIVERSITY AND ECOSYSTEM SERVICES FOR THE AMERICAS 300 by fish, eggs of fish-eating birds, and marine mammals, affecting wildlife reproduction and infant nervous systems. Ocean acidification from increased atmospheric carbon dioxide is increasing and is already impacting major components of the Pacific Ocean food web and contributing to a Caribbean-wide flattening of coral reefs. If current trends continue, coral reef systems will be further adversely affected. Ocean temperatures have become warmer, and together with nutrient run-off, are contributing to increasing ocean deoxygenation. Fossil fuel combustion also contributes to human-caused atmospheric nitrogen deposition, being responsible for 16 per cent of anthropogenic creations of reactive nitrogen, which shifts the species composition of ecosystems and makes groundwater toxic. Fossil fuel related nitrogen emissions have declined in North America. 9 Marine plastic pollution is increasing, and it is expected to exacerbate stresses on the marine food web from warming temperatures, acidification and overexploitation (establisehd but incomplete). In 2010, globally and from land-based sources alone, five to 13 million metric tons of plastic pollution entered the ocean. Two countries of the Americas are among the 20 top polluters. The environmental implications of microplastics at sea are still largely unknown, however the number of marine species known to be affected by this contaminant has gone from 247 to 680 {4.4.2}. New evidence indicates microplastics have a complex effect on marine life and are is transferred up the food chain to people. Impacts on marine wildlife include entanglement, ingestion, death and contamination to a wide variety of species. 10 Human induced climate change caused by the emissions of greenhouse gases is becoming an increasingly more important direct driver, amplifying the impacts of other drivers (i.e. habitat degradation, pollution, invasive species and overexploitation) through changes in temperature, precipitation and frequency of extreme events and other variables (well-established). Climate change has, and will continue to, adversely affect biodiversity at the genetic, species and ecosystem level. The majority of ecosystems in the Americas have already experienced increased mean and extreme temperatures and/or precipitation which have, for example, caused changes in species distributions and ecosystem boundaries, and caused mountain glaciers to retreat. However, the interaction between these direct impacts and other direct and indirect drivers are increasing vulnerability of sensitive ecosystems through the interaction of warming temperatures and pollution, as in the example of coral reefs in the Caribbean. The main impacts on terrestrial, freshwater and marine species are the shift in their geographic ranges, and changes in seasonal activities, migration patterns and abundances. Species affected by other drivers are less resilient to climate change and therefore have a high extinction risk. 11 Although most ecosystems in the America’s continue to be degraded, increases in conservation (e.g. protected areas), and in ecological restoration, are having positive effects. Ecological restoration significantly speeds up ecosystem recovery in some cases (well established), but costs can be significant, and full reversal of the adverse impacts of humans on nature is unlikely to be achievable (well established). Evidence from different subregions indicates that structure and functionality of ecosystems recover faster than species richness (particularly in species-rich biomes). Non-material contributions of naature to people may not be restored for some people {4.4.1}. 12 In spite of the pressures of drivers of change on nature and nature’s contributions to people, there are management and policy options that can affect the drivers of change in order to mitigate, and most importantly, to avoid, impacts on different ecosystems (establisehd but incomplete). However, given the current status and trends of drivers, meeting the Aichi targets and Sustainable Development Goals will require stronger and more effective efforts on the parts of the countries across the region. These options and their implementation are context dependent and strongly influenced by values, governance and institutions {4.7}. Such conditions vary substantially across the Americas in relation to social and economic inequity.
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