A guide to representing variability and uncertainty in biodiversity indicators

Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers.

Optimizing Species Richness in Mosaic Landscapes: A Probabilistic Model of Species-Area Relationships

Most landscapes are comprised of multiple habitat types differing in the biodiversity they contain. This is certainly true for human modified landscapes, which are often a mix of habitats managed with different intensity, semi-natural habitats and even pristine habitats. To understand fundamental questions of how the composition of such landscapes affects biodiversity conservation, and to evaluate biodiversity consequences of policies that affect the composition of landscapes, there is a need for models able to translate information on biodiversity from individual habitats to landscape-wide predictions. However, this is complicated by species richness not being additive. We constructed a model to help analyze and solve this problem based on two simple assumptions. Firstly, that a habitat can be characterized by the biological community inhabiting it; i.e., which species occur and at what densities. Secondly, that the probability of a species occurring in a particular unit of land is dictated by its average density in the associated habitats, its spatial aggregation, and the size of the land unit. This model leads to a multidimensional species-area relation (one dimension per habitat). If the goal is to maximize species diversity at the landscape scale (γ-diversity), within a fixed area or under a limited budget, the model can be used to find the optimal allocation of the different habitats. In general, the optimal solution depends on the total size of the species pool of the different habitats, but also their similarity (β-diversity). If habitats are complementary (high β), a mix is usually preferred, even if one habitat is poorer (lower α diversity in one habitat). The model lends itself to economic analyses of biodiversity problems, without the need to monetarize biodiversity value, i.e., cost-effectiveness analysis. Land prices and management costs will affect the solution, such that the model can be used to estimate the number of species gained in relation to expenditure on each habitat. We illustrate the utility of the model by applying it to agricultural landscapes in southern Sweden and demonstrate how empirical monitoring data can be used to find the best habitat allocation for biodiversity conservation within and between landscapes.

Fire and Its Interactions With Other Drivers Shape a Distinctive, Semi-Arid ‘Mallee’ Ecosystem

Fire shapes ecosystems globally, including semi-arid ecosystems. In Australia, semi-arid ‘mallee’ ecosystems occur primarily across the southern part of the continent, forming an interface between the arid interior and temperate south. Mallee vegetation is characterized by short, multi-stemmed eucalypts that grow from a basal lignotuber. Fire shapes the structure and functioning of mallee ecosystems. Using the Murray Mallee region in south-eastern Australia as a case study, we examine the characteristics and role of fire, the consequences for biota, and the interaction of fire with other drivers. Wildfires in mallee ecosystems typically are large (1000s ha), burn with high severity, commonly cause top-kill of eucalypts, and create coarse-grained mosaics at a regional scale. Wildfires can occur in late spring and summer in both dry and wet years. Recovery of plant and animal communities is predictable and slow, with regeneration of eucalypts and many habitat components extending over decades. Time since the last fire strongly influences the distribution and abundance of many species and the structure of plant and animal communities. Animal species display a discrete set of generalized responses to time since fire. Systematic field studies and modeling are beginning to reveal how spatial variation in fire regimes (‘pyrodiversity’) at different scales shapes biodiversity. Pyrodiversity includes variation in the extent of post-fire habitats, the diversity of post-fire age-classes and their configuration. At regional scales, a desirable mix of fire histories for biodiversity conservation includes a combination of early, mid and late post-fire age-classes, weighted toward later seral stages that provide critical habitat for threatened species. Biodiversity is also influenced by interactions between fire and other drivers, including land clearing, rainfall, herbivory and predation. Extensive clearing for agriculture has altered the nature and impact of fire, and facilitated invasion by pest species that modify fuels, fire regimes and post-fire recovery. Given the natural and anthropogenic drivers of fire and the consequences of their interactions, we highlight opportunities for conserving mallee ecosystems. These include learning from and fostering Indigenous knowledge of fire, implementing actions that consider synergies between fire and other processes, and strategic monitoring of fire, biodiversity and other drivers to guide place-based, adaptive management under climate change.

Modeling variability in the fire response of an endangered bird to improve fire-management

Conservation managers regularly burn vegetation to regenerate habitat for fire-dependent species. When determining the time since fire at which to burn, managers model change in a species' occurrence over time, post-fire (fire-response curve) and identify the time since fire associated with decline in occurrence. However, where species exhibit variability in their fire response across space, using a single fire-response curve to determine the timing of burns may lead to burning habitat at an inappropriate time since fire. We tested if elevation, local topography, soil properties, vegetation type or evapotranspiration affect the fire response of the endangered Mallee Emu-wren Stipiturus mallee and its hummock-grass habitat Triodia scariosa in southeastern Australia (n = 217). Previous work on the Mallee Emu-wren found a unimodal fire response with decline in occurrence at ~30-50 yr since fire and a time window of occurrence of ~30 yr. We found that time since fire and elevation interact to affect the Mallee Emu-wren fire response. At high elevations (55-98 m), Mallee Emu-wrens declined in occurrence at ~50 yr since fire, with a time window of occurrence of 20-40 yr. However, at low elevations (28-55 m), Mallee Emu-wrens showed no decline in occurrence with increasing time since fire with a time window of occurrence of up to 107 yr. Extent cover of Tall T. scariosa showed similar patterns to the Mallee Emu-wren, indicating that vegetation structure is a likely driver of variability in the Mallee Emu-wren fire response. We speculate that the effect of low elevation is mediated by increased soil nutrient and water availability for key plants. We used our findings to map the appropriate time since fire at which to burn to regenerate habitat for the Mallee Emu-wren across the study region. We recommend no burning for regeneration across one-third of potential habitat, because the Mallee Emu-wren showed no decline in occurrence in these areas. We recommend managers model variability in species' fire responses across space to improve the timing of burns for regeneration.