Making sense of the minefield of footprint indicators

In recent years, footprint indicators have emerged as a popular mode of reporting environmental performance. The prospect is that these simplified metrics will guide investors, businesses, public sector policymakers and even consumers of everyday goods and services in making decisions which lead to better environmental outcomes. However, without a common “DNA”, the ever expanding lexicon of footprints lacks coherence and may even report contradictory results for the same subject matter.(1) The danger is that this will ultimately lead to policy confusion and general mistrust of all environmental disclosures. Footprints are especially interesting metrics because they seek to express the environmental performance of products and organizations from a life cycle perspective. The life cycle perspective is important to avoid misleading claims based only on a selected life cycle stage. For example, the water used to manufacture beverages may be important, but if a beverage includes sugar, irrigation water used to cultivate sugar cane could be a greater concern. The focus on environmental performance distinguishes footprints from technical efficiency measures, such as energy use efficiency or water use efficiency, which typically only make sense when applied to a single life cycle stage as they lack local environmental context. However, unlike technical efficiency, which can usually be accurately measured and verified, footprint indicators, with their wider view of environmental performance, are usually calculated using models which can differ in scope, complexity and model parameter settings. Despite the noble intention of using footprints to evaluate and report environmental performance, the potential inconsistency between different approaches acts as a deterrent to use in many public policymaking and business contexts and can lead to confusing and contradictory messages in the marketplace

Impact of climate change on alpine vegetation of mountain summits in Norway

Climate change is affecting the composition and functioning of ecosystems across the globe. Mountain ecosystems are particularly sensitive to climate warming since their biota is generally limited by low temperatures. Cryptogams such as lichens and bryophytes are important for the biodiversity and functioning of these ecosystems, but have not often been incorporated in vegetation resurvey studies. Hence, we lack a good understanding of how vascular plants, lichens and bryophytes respond interactively to climate warming in alpine communities. Here we quantified long-term changes in species richness, cover, composition and thermophilization (i.e. the increasing dominance of warm-adapted species) of vascular plants, lichens and bryophytes on four summits at Dovrefjell, Norway. These summits are situated along an elevational gradient from the low alpine to high alpine zone and were surveyed for all species in 2001, 2008 and 2015. During the 15-year period, a decline in lichen richness and increase in bryophyte richness was detected, whereas no change in vascular plant richness was found. Dwarf-shrub abundance progressively increased at the expense of lichens, and thermophilization was most pronounced for vascular plants, but occurred only on the lowest summits and northern aspects. Lichens showed less thermophilization and, for the bryophytes, no significant thermophilization was found. Although recent climate change may have primarily caused the observed changes in vegetation, combined effects with non-climatic factors (e.g. grazing and trampling) are likely important as well. At a larger scale, alpine vegetation shifts could have a profound impact on biosphere functioning with feedbacks to the global climate

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

Abstract Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m² and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology