Grégoire Certain

The Nature Index : a general framework for synthesizing knowledge on the State of Biodiversity

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.

Who eats whom in the Barents Sea: a food web topology from plankton to whales

A food web is an ecological network and its topological description consists of the list of nodes, i.e., trophospecies, the list of links, i.e., trophic interactions, and the direction of interactions (who is the prey and who is the predator). Food web topologies are widely used in ecology to describe structural properties of communities or ecosystems. The selection of trophospecies and trophic interactions can be realized in different manners so that many different food webs may be constructed for the same community. In the Barents Sea, many simple food webs have been constructed. We present a comprehensive food web topology for the Barents Sea ecosystem, from plankton to marine mammals. The protocol used to compile the data set includes rules for the selection of taxa and for the selection and documentation of the trophic links. The resulting topology, which includes 244 taxa and 1589 trophic links, can serve as a basis for topological analyses, comparison with other marine ecosystems, or as a basis to ...

The Norwegian Nature Index – state and trends of biodiversity in Norway

The aim of the Norwegian Nature Index (NI) is to provide an overview of the state of biodiversity within and across major ecosystems. The index is composed of a series of indicators, each representing individual species or diversity measures. The indicators are standardized and scaled in relation to a reference state, and combined for ecosystems or geographical regions, to give a number between 1 (reference state) and 0 (seriously degraded biodiversity). In 2010, the state of biodiversity was highest in mountains, ocean, coastal waters, and freshwater (NI=0.69–0.80), intermediate for mires and wetlands (NI=0.55), and lowest for open lowlands and forests (NI=0.43–0.44). The NI increased 8–10% in freshwater and the ocean (bottom and pelagic) from 1990–2010, but decreased by>10% in open lowlands during the same period. Since its launch in September 2010, the Nature Index has been approved by the Ministry of Finance as an indicator for biodiversity in the set of sustainable development indicators and approved by the Ministry of Environment as an indicator of the state of major ecosystems.

The Norwegian Nature Index – conceptual framework and methodology

The Norwegian Nature Index (NI) is a general, integrated framework developed to synthesize and communicate the current knowledge of the state and development of biodiversity. It is designed to make the most of the available knowledge in the ecological research community, including expert judgment. The authors present the basic concepts and definitions of the NI, the associated quantitative expressions, and the practical implementation of data collection and integration of expert judgment and data on biodiversity in Norway. The NI can be implemented in data-rich and data-poor areas, it contains information on both the state of biodiversity and the state of knowledge, and it can be aggregated or disaggregated to address specific management themes, which gives the framework the potential to become an efficient management tool.

Estimating inter-group interaction radius for point processes with nested spatial structures

A statistical procedure is proposed in order to estimate the interaction radius between points of a non-stationary point process when the process can present local aggregated and regular patterns. The model under consideration is a hierarchical process with two levels, points and clusters of points. Points will represent individuals, clusters will represent groups of individuals. Points or clusters do not interact as soon as they are located beyond a given interaction radius, and are assumed to interact if their distance is less than this interaction radius. Interaction radius estimation is performed in the following way. For a given distance, observations are split into several clusters whose in-between distances are larger than this distance. For each cluster, a neighbourhood and an area in which this cluster is randomly located is defined under the assumption that the distance between the cluster and its neighbourhood is larger than the interaction radius. The p-value of a test of this assumption is then computed for each cluster. Modelling the expectation of this p-value as a function of the distance leads to an estimate of the interaction radius by a least-square method. This approach is shown to be robust against non-stationarity. Unlike most classical approaches, this method makes no assumption on the point spatial distribution inside the clusters. Two applications are presented in animal and plant ecology.