Conceived and designed the experiments: GC OS SN. Performed the experiments: GC OS SN. Analyzed the data: GC. Contributed reagents/materials/analysis tools: GC OS JWB EF ML JEN AN EO HCP AKS GM IA SE PAG PK ML NGY SN. Wrote the paper: GC. Reviewed/edited the Ms: OS JWB EF ML JEN AN EO HCP AKS GM IA SE PAG PK ML NGY SN.
Current address: Institute of Marine Research, Tromsø, Norway
The authors have declared that no competing interests exist.
The magnitude and urgency of the biodiversity crisis is widely recognized within scientific and political organizations. However, a lack of integrated measures for biodiversity has greatly constrained the national and international response to the biodiversity crisis. Thus, integrated biodiversity indexes will greatly facilitate information transfer from science toward other areas of human society. The Nature Index framework samples scientific information on biodiversity from a variety of sources, synthesizes this information, and then transmits it in a simplified form to environmental managers, policymakers, and the public. The Nature Index optimizes information use by incorporating expert judgment, monitoring-based estimates, and model-based estimates. The index relies on a network of scientific experts, each of whom is responsible for one or more biodiversity indicators. The resulting set of indicators is supposed to represent the best available knowledge on the state of biodiversity and ecosystems in any given area. The value of each indicator is scaled relative to a reference state, i.e., a predicted value assessed by each expert for a hypothetical undisturbed or sustainably managed ecosystem. Scaled indicator values can be aggregated or disaggregated over different axes representing spatiotemporal dimensions or thematic groups. A range of scaling models can be applied to allow for different ways of interpreting the reference states, e.g., optimal situations or minimum sustainable levels. Statistical testing for differences in space or time can be implemented using Monte-Carlo simulations. This study presents the Nature Index framework and details its implementation in Norway. The results suggest that the framework is a functional, efficient, and pragmatic approach for gathering and synthesizing scientific knowledge on the state of biodiversity in any marine or terrestrial ecosystem and has general applicability worldwide.
The magnitude and urgency of the biodiversity crisis is widely recognized within
scientific and political organizations
Previous attempts to provide integrated measures of biodiversity have included GLOBIO
The NI framework collates tractable, calibrated, and scientific information on
biodiversity and the state of ecosystems from a network of experts within all fields
of biomonitoring and ecological research; this network is referred to as the
Ecological Research Network (ERN). The framework synthesizes scientific information
from diverse sources and presents it in a transparent form in order to improve
accessibility for environmental managers, policymakers, and the public. The NI
framework allows for the comparison, application, and traceability of information
from any ecosystem type by optimizing the use of existing information by
incorporating expert judgment and monitoring-based and model-based estimates to
provide a scientific overview that assists environmental managers and policymakers
to set monitoring priorities and objectives. This also facilitates the
identification and quantification of the extent to which knowledge on specific areas
or ecosystems is lacking, which is essential for optimizing research priorities. The
network of scientific experts chosen to represent the ERN are each responsible for
one or more biodiversity indicators. The resulting indicator set is believed to
represent the best available knowledge on the state of biodiversity and ecosystems
in any given area
In this study, we present the NI framework and detail its implementation in Norway. The results suggest that the framework is an efficient approach for collecting and aggregating information on biodiversity and has potential applicability as a functional, efficient, and pragmatic general approach for gathering and synthesizing scientific knowledge on the state of ecosystems and biodiversity.
In the NI framework, a biodiversity indicator is defined as
“A natural variable related to any aspect of biodiversity, supposed to respond to environmental modification and representative for a delimited area. It is a variable for which a value in a reference state can be estimated. The set of indicators should cover as homogeneously as possible all aspects of biodiversity, and any addition of a new indicator should result in the addition of information.”
Thus, a biodiversity indicator might refer to the density, abundance or
distribution of a population of a single species, a taxonomic, functional or
genetic diversity metric, a demographic or behavioural parameter, or any
other natural parameter fitting the definition. Several indicator-based
assessments of biodiversity or an ecosystem state emphasize the requirement
for using a large number of indicators to ensure broad coverage of many
aspects of ecosystems and biodiversity, i.e., structural, functional, and
taxonomic levels
The use of reference states in the NI framework responds to both theoretical
and pragmatic needs. References provide a context for the interpretation of
each observed indicator value, allowing all observed indicator values to be
comparable on the same scale
“The reference state, for each biodiversity indicator, is supposed to reflect an ecologically sustainable state for this indicator. The reference value, i.e., the numerical value of the indicator in the reference state, is a value that minimizes the probability of extinction of this indicator (or of the species or community to which it is related), maximizes at least one measurable aspect of biodiversity of the natural system to which it is related, and does not threaten any measurable aspect of biodiversity in this or any other natural system.”
Thus, a “measurable aspect of biodiversity” refers to a
biodiversity metric at a specified scale
The observed and reference states of a given indicator can be estimated from
data, either by model prediction or by expert judgment. As in other
approaches to biodiversity assessment
Natural systems are composed of a mosaic of ecosystems, and it is crucial
that they are distinguished explicitly. Within the NI framework, natural
systems are termed “major ecosystems” and are categorized into a
set of nine broad natural system types, i.e., mountain, forest, open
lowland, freshwater, mires and wetland, coast pelagic, coast bottom, ocean
pelagic, and ocean bottom (see
The design of spatial and temporal units must fit with the resolution of the available information and with the objectives of knowledge synthesis and management, which may vary among countries and regions. Our case study section details how appropriate units were specified for the implementation of the NI in Norway.
The observed values, or “states”,
The estimate of the observed state for an indicator is assumed to be randomly
drawn from a statistical distribution
Uncertainty because of the data source can be quantified by comparing the number of monitoring-based or model-based estimates with the number of expert-based estimates. This allows an assessment of deficiencies in the monitoring data set produced by the ERN.
In some cases, knowledge is so sparse that even expert-based judgments cannot
be obtained. The number of documented indicators per spatial unit
Each indicator can be expressed using a specific measurement unit, e.g.,
density, abundance, or species richness. Units must be scaled prior to
averaging across spatial units or major ecosystems. Simulated indicator
values
Three simple scaling models were used to account for different ways of
interpreting an observed indicator value relative to the expected value in a
reference state (
Scaled value when the observed value of a hypothetical indicator ranged between 0 and 150 and when the value in a reference state was 50.
The “optimal” model (
We use the “minimal” scaling model (
We use the “maximal” scaling model (
In previous implementations, no particular weights were applied to any of the
The following rules for weights definition have been implemented in Norway. They have been designed to be readily transferrable to other countries with different data availability.
Our approach addresses the following heterogeneities: indicators specific to
a given major ecosystem versus indicators representative of several major
ecosystems; indicators belonging to different taxonomic, trophic, or
functional groups; well-documented indicators identified by the ERN as
strongly representative of any aspect of biodiversity; and spatial units of
different size. The following four sequential steps are used to control for
these potential heterogeneities (
At the finest level (
At the level of a major ecosystem
At the spatial unit
To aggregate across several spatial units (
For the sake of simplicity, the numbers of functional groups and major ecosystems have been slightly reduced relative to the Norwegian application.
The rules for calculating the weights are based on three criteria: (i) some indicators are known to be of higher importance to biodiversity, (ii) indicators can be classified into groups of equal importance in a major ecosystem, and (iii) no major ecosystem is more important than another.
NI results can be presented at several aggregated levels and the choice of
resolution depends on the underlying question addressed. Presenting the NI
as a single value averaged over the axes
The flexible design of the NI framework lends itself easily to the development of sub-indexes (thematic indexes) that focus on given trophic, taxonomic, or threatened species groups in a specific region or on biodiversity pressures associated with a particular environmental problem. Weights attached to thematic indexes can be binary, in order to reflect the selection of the indicators, major ecosystems, and localities that are relevant to a given theme.
Data were collected in Norway for four years (1950, 1990, 2000, and 2010)
using 430 Norwegian municipalities as spatial units (see
The task of identifying biodiversity indicators involved a succession of
meetings, which were organized according to major ecosystems; experts
selected indicators based on the NI definition and any additional criteria
specifically required for the Norwegian implementation of the NI
Data collection began in late June 2009 and was completed in September 2010,
before publication of the first version of the NI. Data were assembled via a
website connected to an SQL database, which was hosted by the Norwegian
Institute for Nature Research (NINA). A demonstration version of this
website can be found at
Operational definitions (
Experts had to provide lower (25%) and upper (75%) quartiles
for each observed indicator value as a measure of numerical uncertainty, as
suggested by
Geographical information system analyses were used to calculate total
municipality area and the area of each major ecosystem within each
municipality. GIS calculations were based on the major ecosystem definitions
in
We used three values to estimate the statistical distribution for each set
In the Norwegian case study, NI results were communicated as maps specific to
each major ecosystem (steps a and b,
The mean number of indicators documented per municipality was calculated for each data source type (data, model, or expert), date, and major ecosystem to illustrate gaps in the data and to detect uncertainty because of data sources.
Some additional analyses were implemented and they are provided as supporting
material. They concern the effect of our weighting system on the NI values
(
The code used to calculate the NI is available as supporting information
(
A total of 308 indicators were selected by experts and used for calculations
(
Tot | Spe | Key | Red | Comm | Serv | Ext | |
Ocean bottom | 31 | 10 | 5 | 6 | 3 | 26 | 4 |
Ocean pelagic | 40 | 16 | 7 | 7 | 2 | 32 | 5 |
Coast bottom | 48 | 27 | 6 | 5 | 8 | 35 | 8 |
Coast pelagic | 35 | 9 | 5 | 4 | 2 | 27 | 3 |
Open lowland | 57 | 30 | 7 | 12 | 2 | 30 | 4 |
Mires and wetland | 40 | 29 | 6 | 10 | 1 | 22 | 4 |
Freshwater | 42 | 36 | 14 | 14 | 9 | 21 | 4 |
Forest | 72 | 59 | 11 | 12 | 5 | 23 | 5 |
Mountain | 30 | 22 | 7 | 6 | 2 | 16 | 3 |
Understanding how reference states were set across major ecosystems enhances our
understanding of how inferences can be drawn from the indicator set (
CC | Sust | Past | Prec | Prist | Best | Trad | |
Ocean bottom | 4 | 0 | 12 | 6 | 3 | 0 | 6 |
Ocean pelagic | 2 | 0 | 17 | 15 | 3 | 0 | 3 |
Coast bottom | 4 | 0 | 12 | 5 | 22 | 0 | 5 |
Coast pelagic | 1 | 0 | 4 | 23 | 6 | 0 | 1 |
Open lowland | 1 | 1 | 8 | 17 | 24 | 0 | 6 |
Mires and wetland | 0 | 1 | 4 | 0 | 32 | 0 | 3 |
Freshwater | 1 | 2 | 4 | 0 | 27 | 8 | 0 |
Forest | 8 | 2 | 18 | 1 | 40 | 0 | 3 |
Mountain | 5 | 0 | 5 | 0 | 20 | 0 | 0 |
The lowest Norway NI values for 2010 were found in open lowland, forest, and
mires and wetlands (
Grey lines and bars correspond to 95% confidence intervals.
A high proportion of indicator values used for all systems were based on expert
judgments (
The concepts of biodiversity and ecosystem state are strongly linked and it is
commonly accepted that ecosystems with high biodiversity in terms of species,
functions, and structures, are more robust and resilient to environmental
pressure, meaning they are more likely to provide ecosystem services to society
By challenging experts to produce indicators with reference states estimated
using the theoretical and operational definitions, we were able to synthesize a
reference state for Norwegian nature. This ideal natural environment would
contain no harvested stocks at risk of extinction. The abundance, density,
biomass, or area of distribution of most of the species or communities would be
close to pristine conditions or alternatively close to the carrying capacity of
their respective ecosystems. Agricultural practices would sustain biodiversity
and ensure the production of ecosystem services dependent on open areas. This
multi-criterion definition reflects the complexity of both natural and societal
systems that a framework such as the NI must consider
Discrepancies in reference states must be considered when interpreting NI values.
For example, a large number of forest indicators used the concept of pristine
nature as a reference, but this concept was rarely used in oceanic areas (
Not all reference states are directed toward exactly the same situation, but they provide environmental managers with a comprehensive set of reference levels when comparing potential goals and objectives. The optimal biodiversity definition needs not necessarily coincide with an optimal definition from an environmental management or political perspective. The distinction between reference states and management objectives is a crucial aspect of the implementation of the NI framework for management and policy purposes. For instance, management objectives might differ from the reference value in the case of trade off between biodiversity and other needs in the society.
The Norway NI shows that several ecosystems are under threat. In 2010, only three
major ecosystems (ocean bottom, coast pelagic, and freshwater) were estimated to
be in an overall good state with an NI around 0.8 and with the lower end of the
confidence interval still above 0.7 (
Spatial patterns in NI values (
Much more information has been extracted from the Norwegian NI framework case
study than the figures presented in this paper. The complete set of results is
available and thoroughly discussed in
The NI is clearly related to the Dutch Natural Capital Index
Heterogeneities in the indicator set often mirror heterogeneities in knowledge
present within the ERN. A weighting system that controls for these
heterogeneities was required. The true states of ecosystems are unknown, and so
assessing the relevance of our weighting system appears challenging but this
will be an important task in the near future. Comparison between weighted and
unweighted NI calculations (
The development of the NI framework was based on a strong, cooperative process
between scientists, managers, and the NI core team. Definitions and explanations
are provided to the experts, but they were entirely free to choose which
information they enter in the database. The NI core team relied entirely on the
information entered by the experts. Creating reciprocal relationships of trust
and other confidence-building measures between the NI core team and the experts
(
The inclusion of expert-based judgments was useful because it allowed us to cover
information that was previously neglected or only used implicitly. Taken
individually, any expert-based approach is more likely to be biased compared
with a more classical, empirical approach, provided that the latter is conducted
properly. Using a high number of experts is one way to control for these biases.
Calibration experiments with similar expert-estimate collection processes showed
a reasonable accuracy for expert performances
The NI framework can be viewed as an operational and pragmatic reply to calls
from the scientific community for the establishment of a general framework to
monitor biodiversity
Using the national level as the operational scale of implementation of the NI
makes sense. However, it is possible to build the NI at other scales if relevant
indicators and experts can be identified. The framework is general enough so
that several NI projects could be implemented simultaneously and then
aggregated. Indeed, NI values make sense when compared with each other, and the
aggregation of all information (steps a–d,
Reporting on the state of biodiversity can help to clarify questions relating to
the causes of change or the consequences of management actions, and it supports
the development of monitoring programs directed to investigating the causes of
observed declines
Reducing the complexity of information may lead to over-simplistic schemes
The NI satisfies the expectations of the international community
The use of thematic indexes provides information on well-defined topics of societal interest, and prevents the NI from being a general and abstract measure. The explicit measure of uncertainty and the identification of gaps in knowledge are key elements for informing management and directing funding to future research needs. The application of the NI framework to other countries would be straightforward.
Given the high international concern about biodiversity loss at the global scale, a framework such as the NI, if widely applied, has the potential to contribute significantly to the estimation of trends in biodiversity and to the design of corresponding management policies, thereby increasing the efficiency of the societal response to the global threat to biodiversity.
Definitions for the 9 major ecosystems used within the NI framework.
(PDF)
Excel file. Detailed list of indicators collected for the NI project in Norway.
(XLSX)
Examples of practical definitions that can be used to estimate the value of indicators in a reference state.
(PDF)
Practical implementations of the Nature Index.
(PDF)
Effect of the weights on the Nature Index calculation
(PDF)
Evolution through time of NI values per municipalities averaged across oceanic, coast and terrestrial major ecosystems.
(PDF)
Development of thematic indexes within the NI framework
(PDF)
R source code for the implementation of the NI. As an example, data on the indicators related to mountains in Norway are provided. The file can be decompressed with Winrar.
(RAR)
We would like to thank all the experts who have contributed to the documentation of
the 308 indicators and made this work possible. We also sincerely thank Frank
Hanssen and Stefan Blumentrath (Norwegian Institute for Nature Research) for helpful
GIS support, Kristin Thorsrud Teien (Norwegian Ministry of Environment) and Ingrid
Bysveen, Else Løbersli, Knut Simensen, and Bård Solberg (Directorate
for Nature Management) for their advice and support, and Jeanne Certain who provided
invaluable help with figure design. Jan Ove Gjershaug (lynx and mountains), Espen
Lie Dahl (red deer), Erling Solberg (moose), and Terje van der Meeren (coast bottom)
kindly provided photographs used in