A Biodiversity Indicators Dashboard: Addressing
Challenges to Monitoring Progress towards the Aichi
Biodiversity Targets Using Disaggregated Global Data
Xuemei Han1,2*, Regan L. Smyth1, Bruce E. Young1, Thomas M. Brooks1,3,4,5, Alexandra Sánchez de
Lozada1, Philip Bubb6, Stuart H. M. Butchart7, Frank W. Larsen8,9, Healy Hamilton1, Matthew C. Hansen10,
Will R. Turner9
1 NatureServe, Arlington, Virginia, United States of America, 2 Department of Environmental Science and Policy, George Mason University, Fairfax, Virginia, United States
of America, 3 International Union for Conservation of Nature, Gland, Switzerland, 4 World Agroforestry Center, International Center for Research in Agroforestry, University
of Philippines, Los Baños, Laguna, Philippines, 5 School of Geography and Environmental Studies, University of Tasmania, Hobart, Australia, 6 United Nations Environment
Programme World Conservation Monitoring Centre, Cambridge, United Kingdom, 7 BirdLife International, Cambridge, United Kingdom, 8 European Environment Agency,
Copenhagen, Denmark, 9 Conservation International, Arlington, Virginia, United States of America, 10 Department of Geographical Sciences, University of Maryland,
College Park, Maryland, United States of America
Abstract
Recognizing the imperiled status of biodiversity and its benefit to human well-being, the world’s governments committed
in 2010 to take effective and urgent action to halt biodiversity loss through the Convention on Biological Diversity’s ‘‘Aichi
Targets’’. These targets, and many conservation programs, require monitoring to assess progress toward specific goals.
However, comprehensive and easily understood information on biodiversity trends at appropriate spatial scales is often not
available to the policy makers, managers, and scientists who require it. We surveyed conservation stakeholders in three
geographically diverse regions of critical biodiversity concern (the Tropical Andes, the African Great Lakes, and the Greater
Mekong) and found high demand for biodiversity indicator information but uneven availability. To begin to address this
need, we present a biodiversity ‘‘dashboard’’ – a visualization of biodiversity indicators designed to enable tracking of
biodiversity and conservation performance data in a clear, user-friendly format. This builds on previous, more conceptual,
indicator work to create an operationalized online interface communicating multiple indicators at multiple spatial scales. We
structured this dashboard around the Pressure-State-Response-Benefit framework, selecting four indicators to measure
pressure on biodiversity (deforestation rate), state of species (Red List Index), conservation response (protection of key
biodiversity areas), and benefits to human populations (freshwater provision). Disaggregating global data, we present
dashboard maps and graphics for the three regions surveyed and their component countries. These visualizations provide
charts showing regional and national trends and lay the foundation for a web-enabled, interactive biodiversity indicators
dashboard. This new tool can help track progress toward the Aichi Targets, support national monitoring and reporting, and
inform outcome-based policy-making for the protection of natural resources.
Citation: Han X, Smyth RL, Young BE, Brooks TM, Sánchez de Lozada A, et al. (2014) A Biodiversity Indicators Dashboard: Addressing Challenges to Monitoring
Progress towards the Aichi Biodiversity Targets Using Disaggregated Global Data. PLoS ONE 9(11): e112046. doi:10.1371/journal.pone.0112046
Editor: Julia A. Jones, Oregon State University, United States of America
Received April 21, 2014; Accepted October 11, 2014; Published November 19, 2014
Copyright: ß 2014 Han et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All indicator data referenced in the manuscript
can be accessed and viewed through the Biodiversity Indicators Dashboard (http://dashboarddev.natureserve.org/dashboard/map.html) in various data formats
and at various scales, by indicator and by regions.
Funding: This work was supported by awards from the John D. and Catherine T. MacArthur Foundation (URL: http://www.macfound.org/) to NatureServe
addressing "Dashboard assessments: proof-of-concept and baselines" (Grant No. 11-98252-000-INP), "Moving graphical presentation of biodiversity conservation
monitoring indicators from a static proof-of-concept to a dynamic prototype" (Grant No. 12-100096-000-INP), and the bridge grant between the two (Grant
No. 12-102962-000-INP). The funders supported the concept development in study design phase, but had no role in data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* Email: xuemei_han@natureserve.org
indicators to report progress towards reducing pressure on
biodiversity, maintaining and improving the state of biodiversity,
implementing conservation actions to ameliorate biodiversity loss,
and providing benefits to human well-being [4]. Many other
initiatives and multilateral agreements call for similar indicatorbased biodiversity monitoring, including (a) the United Nations
Millennium Development Goal #7 [5] and the draft new
Sustainable Development Goals [6]; (b) intergovernmental treaties
that provide mechanisms for national action and international
Introduction
Resource monitoring has long been recognized as a cornerstone
of biodiversity and conservation science [1,2,3]. In 2010, at the
10th Conference of the Parties of the Convention on Biological
Diversity (CBD), 193 nations agreed to twenty ‘‘Aichi Biodiversity
Targets’’, and in doing so committed to updating their National
Biodiversity Strategies and Action Plans and developing monitoring programs to assess progress [4]. The Aichi Targets rely upon
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A Biodiversity Indicators Dashboard
cooperation, such as the Ramsar Convention on Wetlands [7] and
the Convention on Migratory Species [8], (c) science-policy
interfaces such as the Intergovernmental Platform on Biodiversity
and Ecosystem Services [9]; and (d) partnerships or networks in
support of the above mentioned bodies, such as the Biodiversity
Indicators Partnership [10,11,12], and the Group on Earth
Observations Biodiversity Observations Network Working Group
#9 [13].
Monitoring called for by these programs is essential both to
document biodiversity change over time [14], to shed light onto
key ecological processes [15], and to measure the success or failure
of conservation interventions through counterfactual analysis
[16,17,18,19]. However, most existing monitoring programs have
been designed primarily at localized scales, and often produce
information that is disaggregated, heterogeneous, and nonstandardized when considered at national or regional scales [20].
Monitoring requirements for measuring conservation performance, of the kind necessary to track the Aichi Targets, require
data that transcend the fine temporal, spatial, and organizational
scales commonly addressed in current literature [15].
Documentation of conservation impacts and biodiversity
response must be accomplished in ways that are scientifically
defensible, at appropriate temporal and spatial scales, and simple
enough to inform decision-making by the diverse group of
individuals and organizations working at the intersection of
science and policy. Mounting global evidence shows that
biodiversity loss is continuing at alarming rates [21,22], yet
currently, two thirds of national reports submitted to the CBD lack
evidence-based measures to illustrate changes in the status of
biodiversity [23]. National capacity is often insufficient to measure
many indicators of interest using on-the-ground methods, particularly in developing countries [24]. Even when national data are
available, a lack of standardization across countries can make
regional assessment difficult or impossible [20].
To better understand the challenges to effective biodiversity
monitoring at national and regional scales, and how finer-scale
(e.g. national) data might be integrated into a framework for global
monitoring of biodiversity status and trends, we surveyed local
conservation experts working in areas of high conservation value
on monitoring and capacity needs. Building from the needs
identified in those workshops, we then developed the concept for a
biodiversity indicators dashboard using indicators derived from
global data sets and constructed a dashboard prototype. This is the
first operationalized dashboard to date that communicates
multiple biodiversity indicators at multiple scales, and directly
serves the global need to monitor progress towards Aichi Targets.
Full development of the biodiversity indicators dashboard will
encompass: (1) identification of appropriate indicators, (2) proof of
concept using global data, (3) building the technological
infrastructure necessary to host the dashboard, (4) designing the
visual interface for multiple platforms (i.e. web and mobile users),
and (5) creating systems to support the integration of finer-scale
(regional and national) data. Here, we address in detail steps 1 and
2 of the dashboard design, laying the foundation for a web-based
tool freely available to all with an interest in biodiversity
conservation. A prototype of the tool is now available to the
international conservation community at http://dashboarddev.
natureserve.org, with steps 3–5 being implemented in an on-going
iterative process.
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Methods and Results
1. Study Area
We considered three geographically diverse areas with exceptional biodiversity value, that confront a high degree threat and
that receive significant investment by international conservation
agencies (Figure 1) [25,26]. The Tropical Andes region encompasses the eastern slope of the Andes, containing eight watersheds
of headwater rivers (Japura, Putumayo, Rio Maranon, Ucayali,
Guapore, Madre de Dios/Beni, Amazon, Magdalena) across
Venezuela, Colombia, Ecuador, Peru, and Bolivia. The Great
Lakes region of Africa includes five major watersheds (Lake
Victoria, Upper Nile, Lake Tanganyika, Lake Malawi/Nyasa,
Turkana/Omo) across Ethiopia, South Sudan, Kenya, Uganda,
Democratic Republic of Congo, Rwanda, Burundi, Tanzania,
Zambia, Malawi, and Mozambique. The Greater Mekong region
encompasses the entire Mekong River Basin, spanning China,
Myanmar, Vietnam, Lao P.D.R., Cambodia, and Thailand [25].
We delineated regional boundaries for the Tropical Andes,
African Great Lakes and Greater Mekong regions using hydrological basins derived from HydroSHEDS and compiled by the
UN-FAO, [27,28,29]. We performed analyses at both this regional
scale, and at the national scale for the 22 countries that these three
regions overlap (including areas outside the focal watershed
boundaries).
2. Challenges to Biodiversity Monitoring and Capacity
Needs at Regional and National Scales
We conducted seven consultation workshops in the three study
regions between September 2011 and August 2012 to (1) better
understand the challenges to effective biodiversity monitoring at
national and regional scales, (2) identify gaps in current monitoring
capacity and potential mechanisms for filling those gaps, and (3)
begin to explore mechanisms for integrating local and national
monitoring data into future regional and national biodiversity
indicators. In total, 260 individuals from 20 countries attended at
least one of the workshops, with broad representation from the
public, civil-society, and academic sectors. Invitees included those
with professional responsibility for National Biodiversity Strategies
and Action Plans for monitoring progress towards Aichi Targets,
and managers and technical experts responsible for designing and
conducting biodiversity monitoring programs at multiple scales.
At each workshop, we solicited multiple-choice feedback on two
issues: 1) the spatial scales of monitoring that participants required
to guide their work (regional, national, sub-national, watershed,
and/or site scales); and 2) the status of monitoring of selected
biodiversity indicators for pressure, state, response, and benefits at
the national scale, with answer options of ‘‘Monitored’’, ‘‘Limited
Monitoring’’ (monitoring that has been conducted in some areas
but not systematically done across the country), ‘‘Not Monitored’’,
or ‘‘Unknown’’. Of the 260 workshop participants, 132 (51%)
submitted answers to these written questionnaires, of which 39%
came from the public sector, 45% from civil-society, and 16%
from the academic sector. We also recorded and categorized
responses to open-ended questions addressing (1) the utility of
tracking biodiversity indicators derived from existing global data
with a dashboard approach and (2) national challenges in
developing sustainable biodiversity monitoring.
To identify the preferred scales of monitoring, we tabulated the
frequency of the scales that participants indicated were important.
To quantify the existing capacity for monitoring in each of the
targeted countries, we calculated a score based on the perceived
monitoring status for each biodiversity indicator. The score is
scaled 0 (not monitored) to 1 (monitored), and equals P1 + 0.5P2,
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Figure 1. Study area regions. From left to right: the Tropical Andes, the African Great Lakes, and the Greater Mekong.
doi:10.1371/journal.pone.0112046.g001
with P1 the percent of respondents who answered ‘‘monitored’’
and P2 the percent of respondents who answered ‘‘limited
monitoring’’. We used ANOVA to explore differences of
monitoring status between regions, and a repeated-measures
ANOVA to examine differences in monitoring status among
indicators.
Responses to the questionnaire indicate a strong demand for
reliable information on the state of, and pressures facing,
biodiversity. Regarding scales of monitoring, participants were
most interested in analyses carried out at the site (82%) and
national levels (76%), followed by watershed (71%), sub-national
(68%) and regional levels (65%).
Our questionnaires revealed significant differences in the degree
to which indicators are currently monitored (p,0.001), with
hydrologic measures (average score = 0.40) and species extinction
risk (0.57) less frequently monitored than deforestation (0.72) and
protected area coverage (0.79) (Figure 2). While there were no
differences in the average score across regions (p = 0.88), the status
of monitoring differed widely among nations. Of the 22 countries,
those with the highest overall scores for existing monitoring were
Colombia (0.875), Malawi (0.875), and Thailand (0.75). Countries
with very limited monitoring include South Sudan and the D. R.
Congo (both #0.25).
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Among the open-ended questions, a third of survey respondents
from all sectors expressed high interest in using the dashboard
approach, and employing appropriate subsets of global scale data,
as a means to gather and share information to assess biodiversity
status and threats, assess and improve conservation impacts, and
inform policy, planning, and decision-making. Supporting capacity building, promoting stakeholder participation and dissemination of information were also frequently cited by survey
respondents as potential benefits of this effort (Figure 3).
Across regions, the challenges to effective monitoring (Figure 4)
include the lack of personnel, technology, and financial support for
data collection and management (45%), and limited information
accessibility and interoperability (40%). Emphasis varies among
regions, with African respondents stressing the need for support in
data management (24%), and Andean respondents more concerned about scientific standards and methods (25%) and
conservation expertise and analysis (39%).
3. Creation of a Biodiversity Indicators Dashboard to
Support Monitoring Needs
3.1 The Dashboard Concept. To address the challenges to
biodiversity monitoring at regional and national scales identified
by the survey, we envision the creation of a biodiversity
‘‘dashboard’’ – a visualization of biodiversity indicators designed
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Figure 2. Monitoring status of the indicators, as reported by national experts via questionnaire responses. The mean score and its
standard error for each indicator are shown by region. Number of respondent is 36 for Tropical Andes, 46 for African Great Lakes, and 50 for Greater
Mekong.
doi:10.1371/journal.pone.0112046.g002
to enable ongoing tracking of biodiversity status and trends and
present biodiversity monitoring and conservation performance
information in a clear, user-friendly, and unified format that
facilitates iterative adaptive management. Using biodiversity
indicators of the type developed by Butchart et al. (2010) [22] as
the foundation, the dashboard provides a means to disseminate
information, promote stakeholder participation, support capacity
building, and allow users to better understand the relationship
between conservations actions and impact. The utility of the
biodiversity indicators dashboard in meeting these needs was
confirmed by responses to our survey (Figure 3).
Originating as a business performance monitoring tool,
dashboard visualizations are an information management and
reporting instrument that has seen increasing use in a variety of
contexts to communicate complicated information on current
status and historical trends to broad audiences [30,31]. From the
World Bank Atlas of Global Development [32] to commercial
products used to track stock performance and guide financial
investment (e.g., [33]), dashboards distill complicated data by
tracking key indicators, usually via a combination of charts and
maps. This information is typically served on websites or mobile
applications and updated regularly (e.g., annually for World Bank
indicators, minute-by-minute for financial markets).
Dashboards have been proposed and employed in various
biological and resource management contexts. For example, the
CITES Trade Data Dashboard [34] allows users to explore
patterns in species exploitation across space, time, and taxonomic
affiliation through a dynamic interface [35]. Dashboards also
support fisheries management by providing a framework to better
visualize relationships among fish populations, socio-economics,
and exploitation [36,37].
If a dashboard is to be useful for decision makers, the indicators
chosen must present information critical to influencing the
decisions to be made. We used the Pressure-State-ResponseBenefit (PSRB) framework to guide selection of indicators,
following Sparks et al. 2011 [14]. This is derived from the
causal-chain Pressure-State-Response and Driver-Pressure-StateImpact-Response frameworks, widely used for reporting on the
state of the environment [22,23,38,39,40,41,42,43], and one that
has been used by the CBD Ad-Hoc Technical Expert Group of the
CBD to guide indicator development for the CBD [44] and
recommended for communicating biodiversity indicators [42].
The core elements in PSRB as applied in the dashboard
assessments are pressure on biodiversity, its drivers, (e.g., habitat
destruction, climate change, invasive species), the state of species
and ecosystems (e.g., species extinction risk, animal and plant
populations, ecosystem integrity), conservation action or policy
responses (e.g., protected areas establishment and management,
investment in biodiversity conservation) and benefit to human
well-being from the social, economic and cultural impacts of
conservation (e.g., maintenance of hydrological functions, climate
change mitigation, maintenance of indigenous cultures). By
Figure 3. Perceived benefits of using global data within a dashboard approach, by sector. Number of respondent is 51 for public sector,
60 for civil-society, and 21 for academic sector.
doi:10.1371/journal.pone.0112046.g003
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Figure 4. Perceived challenges to biodiversity monitoring by region. Number of respondent is 36 for Tropical Andes, 46 for African Great
Lakes, and 50 for Greater Mekong.
doi:10.1371/journal.pone.0112046.g004
viewing dashboard indicators together within the PSRB framework, users can begin to understand interactions between
indicators [14].
mean value for forest cover in 2000 and mean GFCL between
2000 and 2005. We then derived the average annual rate of GFCL
for 2000–2005 for each analysis unit, presented as the annual
percent forest loss from the 2000 baseline.
While, to our knowledge, the GFCL data provide the best
globally consistent spatial representation of deforestation to date,
the data are limited in that they do not incorporate information on
forest gain from restoration, natural regrowth, and plantation.
They also do not address finer resolution forest degradation, as
some other regional mapping products do [51,52,53].
b. State Indicator: Species Extinction Risk
The Red List Index (RLI) is a measure of trends in survival
probability (the inverse of extinction risk) for sets of species. It is
based on the numbers of species within each IUCN Red List
category (i.e., Extinct (EX), Extinct in the Wild (EW), Critically
Endangered (CR), Endangered (EN), Vulnerable (VU), Near
Threatened (NT), Least Concern (LC)) and the changes in these
numbers over time resulting from genuine improvement or
deterioration in status between assessments [22,54,55,56,57,58].
For this indicator, we used the first and last comprehensive Red
List assessment (when all species of a taxonomic group were
assessed) for each of three vertebrate groups (1988 and 2008 for
birds, 1996 and 2008 for mammals, and 1980 and 2004 for
amphibians, noting that the 1980 assessment for amphibians was
based on a retrospective assessment), following Butchart et al.
(2004, 2005, 2007 and 2010) [22,54,55,56]. We identified all
species falling partially or completely within each region and each
country using 2010 spatial distribution data for each species [58]
(Table 2). For each region and country, we calculated the RLI for
each taxonomic group individually and for all taxonomic groups
together. This standardized RLI varies between 1 (all species LC)
and 0 (all species EX or EW). Following Butchart et al. 2004, 2005
[55,56], species undergoing genuine Red List category changes
between assessments contributed to RLI trends only if the driving
process of the change (i.e. threat or conservation action) operated
within the relevant country or region. For each vertebrate group,
we calculated the annual change in aggregate extinction risk by
dividing the difference in RLI between the last and first assessment
by the number of intervening years. Data Deficient and Not
Evaluated species were excluded from this calculation and the
annual change value across all taxonomic groups is computed
using the mean time difference between assessments for the three
groups.
3.2 Methods for Constructing a Biodiversity Indicators
Dashboard using Global Data. We selected one indicator
from each PSRB component, with consideration for the availability of global datasets, the degree to which the indicator contributes
to evaluating progress made towards the Aichi Targets, the
feasibility of trend estimates, and the likely availability of
analogous data generated locally for future integration into the
dashboard. The four selected indicators are examples of the types
of data the biodiversity indicators dashboard can be used to track.
At this stage in development of the dashboard, the chosen
indicators are not intended to address causal relationships;
however, as additional indicators are added, the PSRB approach
will facilitate exploration of causal links between indicators. We
selected forest cover loss as the pressure indicator, species
extinction risk as the state indicator, protected area coverage of
key biodiversity areas (KBAs) as the response indicator, and
freshwater provisioning to downstream human populations as the
benefit indicator. We used data from the first decade of the 21st
century to represent current status and provide an initial baseline,
or reference point, against which future trends can be assessed. For
all but the benefit indicator (freshwater provision), existing data
from either previous time steps (i.e. species extinction risk and
percent protection of KBAs) or later time steps (i.e. forest cover
loss) supporting the tracking of trends.
Global data were disaggregated to provide regional and
national indicator values (Table 1). For each indicator, we mapped
current condition, charted and mapped trends over time, and
generated tabular summaries. All spatial analyses were performed
using ArcGIS 10.1 [45] and all statistical calculations were
performed in R [46].
a. Pressure Indicator: Forest Loss
The forest loss indicator is derived from the Global Forest
Monitoring Project [47,48], which estimated forest cover in 2000
and forest cover loss between 2000 and 2005 using MODIS data
[49] calibrated with Landsat [50] imagery, at 18.5-km resolution.
Values represent the percent forest cover within each pixel, with
forest cover defined as areas with at least 25% cover of trees at
least 5 meter in height. The deforestation measure, Gross Forest
Cover Loss (GFCL), represents a unidirectional change in forest
cover, calculated from the percent forest loss between 2000 and
2005. For each analysis unit (e.g., region, nation) we calculated a
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Table 1. Biodiversity indicators summary and data sources.
Framework Component
Pressure/Driver
State
Response
Benefit (Impact)
Indicator
Forest coverage and rate
of gross forest cover loss
Red List Index
Protected area coverage
in key biodiversity areas
Quality-weighted freshwater
provision from natural
ecosystems to downstream
human population
Aichi Target
Target 5: Loss of habitats is
at least halved by 2020
Target 12: Extinctions of
known threatened species
has been prevented by
2020
Target 11: At least 17% of
terrestrial…areas, especially
important areas for
biodiversity and ecosystem
services are conserved by
2020
Target 14: Ecosystems that
provide essential services are
restored and safeguarded by
2020
What does it show
Spatial data represents the percent
of forest cover for each 18.5 km
pixel in 2000 and percent gross
forest cover loss (i.e., deforestation)
from 2000 to2005. Tabular FAO
data summarize forest land use
coverage and "net forest cover
change" by country in 2005
and 2010.
An index of aggregate
survival probability of
species that occur in the
given spatial unit. Values
range from 1 (all species
Least Concern) to 0 (all
Extinct).
Mean percent area of key
biodiversity areas covered
by protected areas
Quality-weighted delivery of
clean freshwater from natural
habitats to downstream
human populations per unit
area
Data Source
Forest cover for year 2000 and gross
forest cover loss 2000–2005 through
Global Forest Monitoring Project
IUCN Red List
assessment for:
- World Database on
Protected Areas
(UNEP-WCMC) (2010)
- World WaterGAP 2 model
runoff map
- Hydrological drainage
direction
- Amphibians (1980, 2004)
- Global KBAs as represented
by Important Bird and
Biodiversity Areas (IBAs)
and Alliance for Zero
Extinction (AZE) sites
- Landscan Global Population
Database
- Birds (1988, 2008)
- GlobCover land cover
- Mammals (1996, 2008)
Time frame
2000–2005
1980–2008
Limitation and caveat
- Resolution is too coarse
(18.5 km) to detect deforestation
in small areas.
- Differing assessment
- The WDPA omit recently
dates requires interpolation decreed protected areas
and extrapolation to
estimate aggregate trends
1950–2010
2010
- The gross forest cover loss data
shows deforestation only, not
taking account afforestation
- Because of the
heterogeneous distribution
of species, regional
extinction risk can skew
national indicator values
- The WDPA does not
currently document
management effectiveness
- Only baseline (2010) data
currently available; not able
to estimate trend
- Forest degradation was
not quantified
- The proportion of a
species’ range within a
given analysis unit is not
considered
- Key biodiversity areas for
taxa other than birds that
are not endemic to single
sites have only been
identified in some countries
- Spatial resolution is too
coarse (2,5921km2 pixels) to
estimate freshwater provision
in small areas
- Values are relative, not
absolute
- Red List categories are
necessarily broad classes
of extinction risk, so the
RLI is moderately
sensitive
doi:10.1371/journal.pone.0112046.t001
our study area, key biodiversity areas include 757 Important Bird
& Biodiversity Areas (IBAs; the subset of key biodiversity areas
important for birds[62]), and 139 Alliance for Zero Extinction sites
(AZE; the subset of key biodiversity areas holding effectively the
entire populations of highly threatened species, i.e., CR and EN
species.95% restricted to single sites [63,64]). We calculated the
percentage of each KBA that overlaps protected area boundaries
c. Response Indicator: Protected Area Coverage of Key
Biodiversity Areas
For the Response indicator, we calculated the mean percentage
area of key biodiversity areas (sites contributing significantly to the
global persistence of biodiversity [59]) falling within protected
areas for each analysis unit [60]. We used the World Database on
Protected Areas for 2010 [61] to delimit protected areas. Within
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30
71
41
3
17
10
30
142
378
336
1534
1479
642
2499
Greater Mekong
613
2483
African Great Lakes
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and charts visualized to support a web-enabled biodiversity
indicators dashboard (Table 3, Figures 5–7). A web prototype of
the dashboard, displaying the results discussed here, is available at
http://dashboarddev.natureserve.org.
Together, the dashboard graphics present a picture of the
current status (Figure 5) and trends (Figure 6) in biodiversity.
Baseline data show large geographical variation in the status of
forest cover. National forest coverage is the lowest in Kenya
(7.47%), and the highest in D. R. Congo (71.18%) and Lao P.D.R.
(69.06%) (Table 3 and Figure 5A). The baseline Red List Index
reveals high species extinction risk in Tropical Andes for all taxa
(0.89), with variations among countries and among taxonomic
groups (Table 3). The conservation response, measured as the
average percentage of key biodiversity areas under protection,
varies somewhat among regions (44% in the Tropical Andes, 63%
in the African Great Lakes region, and 49% in the Greater
Mekong as of 2010) but the dashboard shows larger differences at
national levels, with lows in Mozambique (20%), Ethiopia (25%),
Peru (25%), Vietnam (34%) and Myanmar (35%), and highs in
Burundi (100%), Malawi (88%), Venezuela (79%) and Thailand
(73%) (Table 3 and Figure 5C). Similarly, baseline data for
freshwater provision show large national differences (Figure 5D),
with Burundi, Rwanda, Vietnam, and China standing out as areas
of high importance.
The trend of forest loss is documented as ongoing in all regions
evaluated, with national rates of loss lowest in Peru (0.08%/yr) and
D. R. Congo (0.12%/yr), and highest in Kenya (1.2%/yr)
(Table 3 and Figure 6A). The Red List Index indicates the
worsening status of species, with a decline in Red List Index
observed for all nations between 1980 and 2008 (Table 2). Rates
of decline were highest in the Tropical Andes, largely driven by
amphibians (1.3661023/yr), and in the Mekong, due to both
mammals (1.2961023/yr) and amphibians (0.9861023/yr). Protection of key biodiversity areas increased in all regions since 1980,
with some regional variation in the rate of increase (0.20% in the
African Great Lakes, 0.86% in the Tropical Andes, and 1.43% in
the Greater Mekong). Nationally, rates of increase in the
doi:10.1371/journal.pone.0112046.t002
1980–2004
137
1988–2008
29
1996–2008
13
1980–2008
179
1499
2004
2008
2978
5357
Tropical Andes
2008
2008
Assessment year
Amphibians
Birds
Mammals
and subsequently generated a national mean. We used the year of
establishment to generate time series graphs for each year from
1950 to 2010, assigning an establishment date to those protected
areas lacking establishment date by randomly sampling from
known dates of designation of protected areas in the same country,
and then bootstrapping following the methods of Butchart et al.
[60]. We plotted the mean with 95% confidence intervals based on
uncertainty arising from the missing data [60]. We also calculated
the annual rate of change in protection of key biodiversity areas
between 1980 and 2010. The annual rate of change is thus from a
time period comparable to that calculated for the State indicator
(the Red List Index).
d. Benefit Indicator: Freshwater Provision
Freshwater provision data were developed by Larsen et al.
[65,66] using spatially explicit maps of runoff from the global
hydrological water model WaterGAP [67], hydrological drainage
directions [27,68], downstream human population density [69],
and global land cover data (used to weight flow estimates by a
quality coefficient, based on information from previous studies
[70,71,72,73]). Estimated quality-weighted freshwater provision,
reported as a freshwater flow index, was calculated for 2,592 km2
hexagonal grid cells [66]. Using this grid, we calculated a mean
value for each analysis unit. Because the freshwater provision data
is currently only available for a single time step (2010), we cannot
yet calculate trends.
3.3 Results of Biodiversity Indicators Dashboard with
Global Data. The indicators are presented as a series of maps
880
Mammals
Overall
Overall
Birds
Number of species with changed Red List category
Number of all assessed species
Table 2. Number of species recorded and analyzed to derive Red List Index (only extant species that are not Data Deficient were included).
Amphibians
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A Biodiversity Indicators Dashboard
Figure 5. Dashboard indicator baseline results. Results for (A) Forest Cover (2000); (B) Red List Index a measure of change in extinction risk
(2008); (C) Protected Area Coverage of Key Biodiversity Areas (2010); and (D) Freshwater Provision (2010).
doi:10.1371/journal.pone.0112046.g005
protection of key biodiversity areas ranged from lows of zero in
Burundi, Malawi, Mozambique and Rwanda, and highs of 2.09%,
1.54%, and 1.42% in Lao P.D.R., China, and Cambodia (Table 3
and Figure 6C).
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Discussion
1. National and Regional Monitoring Challenges
As approaches to biodiversity management shift towards dataintensive and science-driven methods [74,75], addressing gaps in
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November 2014 | Volume 9 | Issue 11 | e112046
A Biodiversity Indicators Dashboard
Figure 6. Dashboard indicator trend results. Annual rate of (A) Gross Forest Cover Loss (2000–2005); (B) Change in Red List Index as a measure
of extinction risk (change for all species of mammals, birds, and amphibians; 1980–2008); and (C) Change of Protected Area Coverage of Key
Biodiversity Areas (1980–2010).
doi:10.1371/journal.pone.0112046.g006
measure necessary to meet current demand. Moving forward, the
respondents noted that it will be necessary to augment and validate
globally-derived measures with national and local monitoring
results, and doing so will require both cost-effective participatory
monitoring protocols that ensure sustainable data collection and
well-designed standards that ensure data interoperability (Figure 4). A lack of baseline data was the most frequently mentioned
monitoring challenge in our survey. The few studies that have
systematically evaluated the availability of indicators for monitoring biodiversity targets [90,91] support our findings that biodiversity indicators, particularly indicators of state and benefit, are
deficient (Figure 2). Unstable political situations, lack of financial
support, and the low priority of biodiversity monitoring culturally
or in national development strategies all can impair continuous
and systematic data collection. At the same time, the barriers to
information access and interoperability, prevent the information
that does exist from fully informing conservation efforts. Biodiversity data are generated and kept by different agencies in a
fragmented manner. Within our area of study, civil society and
capacity for information generation and dissemination has become
increasingly important [76,77,78,79,80]. The prevailing and
widely recognized challenges to addressing these gaps include
sustaining financial and human resources for on-the-ground
monitoring [76,81], overcoming cultural and technical barriers
associated with data generation [82,83,84] and information
sharing [74,85,86], adopting scientific standards for monitoring
and data analysis [79,82,84,87], and developing indicator sets that
can effectively inform policy and decision-making processes
[10,40,88,89]. Our survey of regional biodiversity experts
reaffirms these challenges in generating, managing, and sharing
biodiversity information (Figure 4), demonstrates the strong
demand for access to biodiversity status and trend data at multiple
spatial scales, and indicates that our proposed biodiversity
indicators dashboard could be an effective tool to address varied
conservation needs (Figure 3).
Recognizing that national and local indicator data are often
limited or non-existent, the survey respondents affirmed the value
of deriving indicators from global datasets as an intermediate
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A Biodiversity Indicators Dashboard
Figure 7. Dashboard indicator trend graphs by region. A.1 – A.3 chart gross forest loss as a percent of forest cover in 2000; B.1-B.3 chart
change in Red List Index for mammals (green), birds (red), and amphibians (blue); and C.1-C3 chart change in protected area coverage of key
biodiversity areas (1950–2010) with solid lines indicating the mean percent protected across all sites, and dashed line indicating the 95% confidence
intervals [60].
doi:10.1371/journal.pone.0112046.g007
utility of biodiversity indicators in monitoring conservation status
and trends [14,22], our biodiversity indicators dashboard is the
first operationalized online interface that communicates multidimensional indicators with spatial representation. By providing
easy access to indicator information at national and local scales, it
complements global efforts such as the Biodiversity Indicators
Partnership [12] and facilitates reporting for Aichi National
Biodiversity Strategies and Action Plans (NBSAPs). The intuitive
graphics and ease of data access that flow from the dashboard are
intended to engage and enhance partnerships at all levels –
international, regional, national, and local.
As a data visualization tool, the biodiversity indicators
dashboard is designed so that a quick examination communicates
the overall status of biodiversity conservation, important trends
and patterns, and previously hidden challenges. For example, the
graphics and values for the state indicator, such as those presented
in Table 3 and Figure 7, communicate a high extinction risk in the
Tropical Andes, driven largely by the extinction risk of amphibian
species. This finding is consistent with other recent studies [97,98]
and highlights the importance of addressing threats to amphibian
species if biodiversity is to be maintained.
By using data and methods that are globally consistent, the
dashboard facilitates direct comparison of baseline and trends
across regions and nations in the three continents targeted for this
first stage of dashboard developments. Regional patterns are
readily evident, such as the higher rate of decline of the Red List
Index in the tropical Andes or the enormous importance of fresh
water provisioning in the Greater Mekong countries (Figure 5).
academics (such as the Wildlife Conservation Society in Cambodia, NatureKenya in Kenya, NatureUganda in Uganda) accumulate a wide range of site-level monitoring data maintained as
project-based resources, while national level monitoring data are
typically held by specific government divisions who may or may
not share that information with other government entities, much
less outside organizations. These challenges are well-known and
efforts to address them are being made at global (e.g., Biodiversity
Indicators Partnership [12]), regional (e.g., ASEAN Centre for
Biodiversity [92], Red Amazónica de Información Socioambiental
Georreferenciada [93], Streamlining European Biodiversity Indicators [94], Conservation of Arctic Flora and Fauna [95]), and
national levels (e.g., National Biodiversity Data bank in Uganda
[96], National Biodiversity Database System in Vietnam).
However, those efforts do not cover all countries and regions,
and ready access to reliable and geographically consistent
biodiversity indicator information at multiple scales remains a
confirmed need.
2. Towards a Dashboard
The biodiversity indicators dashboard, as explained here, is
designed to address the unmet needs expressed in our survey of
biodiversity experts by laying the foundation for better accessibility
and interpretability of existing biodiversity trend data within a
framework that enhances monitoring capacity and promotes data
interoperability and sharing. Many of these indicators are widely
used at global scales, but until now have rarely been reported at
national scales. While previous studies have demonstrated the
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Table 3. Baseline and trend results for all indicators by country and region.
Indicators – Baseline
Country/Region
Bolivia
Indicators – Annual Rate of Change
Protection
Coverage of
KBAs %
Freshwater
(2010)
Flow Index
Forest
Coverage %
Red List Index of species survival
(2000)
56.26
GFCL %
(2000–
2005)
3 taxa
(2008)
Mammal
(2008)
Bird
(2008)
Amphibian
(2004)
0.97
0.96
0.98
0.90
51.51
1.13
0.21
Red List Index of species survival (x 0.001)
3 taxa
(1980–
2008)
Mammal
Bird
(1996–2008) (1988–2008)
Amphibian
(1980–2004)
20.16
20.19
20.62
20.06
Protection
Coverage of
KBAs %
(1980–2010)
1.03
Colombia
56.06
0.92
0.93
0.97
0.77
42.15
4.82
0.22
20.38
20.35
20.04
21.09
0.59
Ecuador
50.1
0.92
0.93
0.97
0.73
51.77
6.56
0.29
20.44
20.39
20.03
21.57
1.24
Peru
52.23
0.94
0.93
0.97
0.82
25.07
2.89
0.08
20.23
20.21
20.05
20.91
0.61
Venezuela
53.95
0.95
0.95
0.98
0.80
79.34
3.3
0.23
20.24
20.32
20.02
21.15
1.05
Tropical Andes
64.04
0.89
0.92
0.95
0.73
44.10
2.06
0.23
20.51
20.24
20.07
21.36
0.86
Burundi
19.67
0.96
0.95
0.97
0.88
100
29.13
0.51
20.07
0.00
20.10
0.00
0
11
D.R. Congo
71.18
0.97
0.95
0.98
0.96
55
3.25
0.12
20.09
20.27
20.07
0.00
0.17
Ethiopia
14.27
0.96
0.92
0.98
0.91
24.55
6.76
0.58
20.20
20.21
20.22
0.00
0.1
Kenya
7.47
0.97
0.95
0.98
0.95
54.60
5.71
1.2
20.18
20.23
20.18
20.10
0.27
Malawi
19.79
0.98
0.98
0.98
0.96
88.42
9.8
0.52
20.13
20.20
20.14
0.00
0
Mozambique
43
0.98
0.97
0.98
0.98
19.70
3.71
0.57
20.13
20.17
20.15
0.00
0
Rwanda
18.92
0.95
0.93
0.97
0.86
38.57
27.48
0.54
20.10
20.18
20.09
0.00
0
South Sudan
22.18
0.99
0.97
0.99
1.00
64
1.39
0.39
20.18
20.32
20.17
0.00
0.45
24.3
0.95
0.93
0.97
0.84
60.97
4.66
0.66
20.14
20.17
20.11
20.23
0.07
21.45
0.98
0.96
0.99
0.95
61.61
8.24
0.45
20.11
20.18
20.11
0.00
0.61
Zambia
38.52
0.98
0.97
0.99
1.00
59.66
2.09
0.47
20.09
20.16
20.09
0.00
0.19
Africa Great Lakes
25.07
0.96
0.94
0.98
0.93
62.89
7.15
0.40
20.14
20.27
20.14
20.05
0.20
Cambodia
49.81
0.94
0.88
0.96
0.96
42.53
8.91
0.57
20.57
21.79
20.37
20.19
1.42
China
13.17
0.92
0.91
0.96
0.79
53.00
11.36
0.45
20.42
20.88
20.14
21.03
1.54
Lao P.D.R.
69.06
0.95
0.88
0.98
0.95
62.80
8.51
0.54
20.40
21.42
20.21
20.33
2.09
Myanmar
50.22
0.95
0.90
0.97
0.97
35.38
9.52
0.55
20.54
21.20
20.47
20.23
0.92
Thailand
31.77
0.95
0.89
0.96
0.96
73.41
9.37
0.81
20.82
21.57
20.72
20.56
1.19
Vietnam
38.27
0.94
0.87
0.96
0.91
33.68
19.44
0.4
20.42
21.45
20.16
20.77
0.99
Greater Mekong
44.03
0.94
0.89
0.97
0.91
49.21
6.34
0.53
20.46
21.29
20.19
20.98
1.43
doi:10.1371/journal.pone.0112046.t003
A Biodiversity Indicators Dashboard
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Tanzania
Uganda
A Biodiversity Indicators Dashboard
Stark differences in the efforts of neighboring countries at
safeguarding key biodiversity areas, such as for Mozambique
and Tanzania, are also easily discernible by a non-scientific
audience (Figure 5).
To develop the dashboard concept, we used indicators derived
from global data to bypass the many obstacles in obtaining
consistent and comprehensive regional and national data. While
the reporting of these global indicators at the national level
provides new and valuable information, there are limitations in
using global data to represent national indicators. While advances
in remote sensing technology provide an unprecedented opportunity to gain temporally repetitive information [99], currently
available estimates of land cover change can differ substantively
among sources (i.e., FAO forest resource assessment [100], Global
Forest Cover Loss mapping [48], ESA Global Land Cover and
GlobCover products [101,102]) and are often coarse in resolution
(342 km2 and 2592 km2 respectively for the deforestation and
freshwater provision datasets used here). With regard to protected
areas, global datasets tend to omit recently decreed areas and often
fail to capture important differences in on-the-ground management. Similarly, the use of global distributions and Red List
categories may not adequately reflect the conservation status of a
given species in a particular region, and because global
distribution data is only available for terrestrial vertebrates, the
results do not reflect the status of many other components of
biodiversity.
The shortcomings of indicators derived from global data can be
addressed by integrating nationally and locally derived data into
the final dashboard design. Despite the numerous obstacles, data
are being generated at a variety of scales deemed useful by survey
respondents, both through governmental efforts [92,96,103,104],
regional consortiums [92,93,94,105,106,107], and site-specific
projects [108,109]. The next stage of the biodiversity indicators
dashboard development focuses on building an effective data
sharing mechanism the promotes shared identifiers to link data
from these different sources [83], digital architecture to coordinate
data flow and ensure data ownership, and promoting consent and
trust among data contributors. With continued development, we
envision the biodiversity indicators dashboard as an interactive,
web-accessible platform that can facilitate national reporting
towards biodiversity targets while allowing for the integration of
localized data to support the type of site-scale monitoring deemed
important to survey participants. The dashboard framework has
also been designed so that over time, the indicators discussed here
can be supplemented with other metrics capturing complementary
aspects of each of state, pressures, responses and benefits (e.g.
population trends, agricultural intensity, environmental legislation
and additional ecosystem service measures).
By serving these data as a web-accessible dashboard, we can put
information on status and trends in biodiversity within easy access
of users and organizations from all sectors and backgrounds, and
facilitate more informed decision-making, enable exploration of
patterns among variables, and support the tracking of progress
towards conservation goals. The maps presented here and
information contained in Table 3 can be depicted in a dashboard
format via various means, including as mapped values (Figures 5
and 6), mouse-over boxes displaying the numerical values
associated with those maps, tabular data summaries by nation or
indicator accessible via interactive menus, and charts of trends that
users could generate either by region (Figure 7) or nation.
In agreeing to the Aichi Targets, the nations of the world
implicitly committed to developing the data necessary to effectively
monitor progress towards meeting biodiversity goals. The challenges in reporting towards those goals are many, but we believe
the dashboard approach, as outlined here, provides a valuable
framework that can facilitate and advance the type of reporting
required by the conservation community. Starting with global
indicators and expanding to incorporate additional national and
site-scale data identified as important by conservation practitioners
on the ground, the biodiversity indicators dashboard can serve as a
tool to track progress towards Aichi Targets, support national
monitoring and reporting, and inform outcome-based policymaking in the realm of conservation.
Acknowledgments
We thank A Chenery, D. Stanwell-Smith, J Tordoff, A. Ajagbe, C. Josse,
the Biodiversity Indicator Partnership, and ASEAN Biodiversity Center for
assistance on consulatation workshops; A. S. L. Rodrigues, T. Evans, R.
Hoft, and C. Josse for comments on the indicator data and manuscript
preparation; the survey respondents; and the many thousands of
individuals and organisations who contribute to the compliation of IUCN
Red List assessments, the World Database on Protected Areas, and the
indentification and documentation of Important Bird and Biodiverseity
Areas and Alliance for Zero Extinction Sites.
Author Contributions
Conceived and designed the experiments: TMB XH BEY ASL. Performed
the experiments: XH ASL TMB BEY. Analyzed the data: XH ASL SHMB
FWL. Contributed reagents/materials/analysis tools: PB SHMB FWL
MCH WRT. Wrote the paper: XH RLS BEY TMB ASL PB SHMB FWL
HH MCH WRT.
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