Biodiversity as insurance: from concept to measurement and application

Abstract

Biological insurance theory predicts that, in a variable environment, aggregate ecosystem properties will vary less in more diverse communities because declines in the performance or abundance of some species or phenotypes will be offset, at least partly, by smoother declines or increases in others. During the past two decades, ecology has accumulated strong evidence for the stabilising effect of biodiversity on ecosystem functioning. As biological insurance is reaching the stage of a mature theory, it is critical to revisit and clarify its conceptual foundations to guide future developments, applications and measurements. In this review, we first clarify the connections between the insurance and portfolio concepts that have been used in ecology and the economic concepts that inspired them. Doing so points to gaps and mismatches between ecology and economics that could be filled profitably by new theoretical developments and new management applications. Second, we discuss some fundamental issues in biological insurance theory that have remained unnoticed so far and that emerge from some of its recent applications. In particular, we draw a clear distinction between the two effects embedded in biological insurance theory, i.e. the effects of biodiversity on the mean and variability of ecosystem properties. This distinction allows explicit consideration of trade-offs between the mean and stability of ecosystem processes and services. We also review applications of biological insurance theory in ecosystem management. Finally, we provide a synthetic conceptual framework that unifies the various approaches across disciplines, and we suggest new ways in which biological insurance theory could be extended to address new issues in ecology and ecosystem management. Exciting future challenges include linking the effects of biodiversity on ecosystem functioning and stability, incorporating multiple functions and feedbacks, developing new approaches to partition biodiversity effects across scales, extending biological insurance theory to complex interaction networks, and developing new applications to biodiversity and ecosystem management.

Keywords: biodiversity; ecosystems; insurance; management; portfolio; stability; theory.

Fig 1

Main economic concepts related to biological insurance and portfolio theories in ecology. (A) Utility and utility functions for risk‐averse, risk‐neutral, and risk‐seeking preferences as the building blocks for the economic concept of insurance. (B) Why risk aversion leads people to buy insurance. The x‐axis shows the amount of something (e.g. dollar value, wealth, or amount of an ecosystem service) and the y‐axis represents the utility of that amount for a risk‐averse person. The outcome is risky, potentially taking on the values X or X–d, where d measures damage (here with equal probability in this simple illustration). The expected outcome is then E(X). Because the person is risk‐averse, the utility of a lower amount of X with certainty is equal to the higher expected value: E(X) = (X + X–d)/2. This point of equivalence is known as the ‘certainty equivalent’ (CE), and the difference between CE and the expected value is the risk premium, or the amount someone is willing to pay to obtain a lower value of X but with certainty. (C) Portfolio theory: when there are trade‐offs between the expected return and its variance (a so‐called ‘risk–return trade‐off’), an efficiency frontier indicates the best expected return possible for a given risk‐tolerance level. (D) Role of diversification of stocks or assets in economics. Diversification reduces unsystematic risk, i.e. risk that differentially affects some stocks or assets more than others when those assets are uncorrelated in their response, but it does not reduce systematic risk, i.e. the risk of shocks that affect all stocks simultaneously (e.g. a market collapse).

Fig 2

Spatial insurance theory: additional stabilising effects on ecosystem functioning that arise from environmental variations across space (two sites 1 and 2). (A) Biodiversity enhances the spatial stability of total biomass or yield (black curves) when different species (red and blue curves) are favoured under different environmental conditions. (B) Biodiversity enhances the spatiotemporal stability of total biomass or yield when different species show compensatory fluctuations across both space and time. (C) Spatial asynchrony of environmental conditions generates asynchronous fluctuations in ecosystem properties across space, thereby stabilising total biomass or yield at the regional scale (as measured by the sum of the two black curves). Horizontal arrows represent dispersal, which helps maintain rare species in a site when environment conditions are unfavourable. The same red and blue species are shown in the two sites for simplicity, but changes in species composition are expected at large spatial scales. Local fluctuations in species contributions to ecosystem functioning are assumed to be periodic for simplicity, but they could be also stochastic, with similar outcomes.

Fig 3

Efficiency frontiers of the productivity of boreal forests in Québec under current climate (left) and a future climate scenario (right). The colour gradient represents variations in the proportions of balsam fir (Abies balsamea) and aspen (Populus tremuloides), from pure balsam fir monocultures (dark red) to pure aspen monocultures (dark blue). Simulations were conducted with the observed/projected variation in annual temperature, total precipitation and drought code (solid lines). We also consider a scenario with reduced variation in climate, corresponding to 0.1 of the observed/projected standard deviation of climate parameters in order to approximate the intercept of the efficiency frontier (risk‐free scenario, dotted lines). Methods: we investigated the effects of climate and competition on basal area increment using growth cores from individual trees sampled in natural forests through the permanent sampling plot survey of Quebec's Ministère des Forêts, de la Faune et des Parcs. We selected 455 sample plots where the two species were present along a gradient from pure stands to perfectly mixed stands. Individual basal area increment (m²/year) was modelled using linear mixed models with fixed effects [annual average temperature, annual total precipitation, drought code, diameter at breast height (DBH), total competition, proportion of interspecific competition, drainage, soil texture] and random effects (individual, plot) (Aussenac, 2017). We then projected annual basal area increment (m²/ha) for a hypothetical stand of 250 trees of 20 cm DBH under current and future climate conditions. We generated 1000 random draws of current and future climate conditions based on the observed average and standard deviation of historical climatic conditions and for climate projections under the RCP8.5 scenario (IPCC's Representative Concentration Pathway corresponding to a radiative forcing of 8.5 W/m² in the year 2100) for an average plot located in the centre of Quebec's boreal forest.

Fig 4

Synthesis of biological and economic insurance and portfolio theories. (A) Shared features across disciplines. The ecological functioning (or economic value) F _i of a community (or portfolio) i depends on its composition x _i ^*, selected from a larger pool of species (or assets) x, whose individual properties fluctuate due to external factors y _i . Theories focus on the aggregated properties of a community (or portfolio) across time, as well as across space (across different communities or portfolios), which are summarized into regional outcomes O. (B) Classic economic portfolio theory encompasses both selection and buffering effects (Table 1), with two main assumptions. First, asset values are set by global market prices and are thus synchronised across portfolios. Second, initial investment is distributed among assets without any form of niche complementarity, which enhances species performance in ecological communities. These assumptions lead to a mean–variance trade‐off, which is less common in ecology. (C) Economic insurance can be conceptualised as initial selection for lower‐return but lower‐variance assets. Options represent delayed selection, allowing future positive selection effects that exploit directional trends in asset value.