Anders Lorentzen Kolstad

In face of the ongoing nature crisis, the international community is setting targets and deciding on actions to combat the current biodiversity crises. For this to be effective they need tools to accurately describe the current situation and to monitor trends in ecosystems over time. Ecosystem condition accounts (ECA) is one such tool that use variables and indicators to describe key ecosystem characteristics, reflecting their condition and deviations from a reference condition. Because the purpose is to inform decisions at relatively high political levels, these metrics are often spatially aggregated to represent larger areas, such as countries. However, spatial aggregation of information has the potential to alter the descriptive and normative interpretations one can make from these metrics. For example, aggregation displacement causes the information held in variables and indicators to diverge when these are aggregated spatially. This process is influenced also by the order of steps involved in normalising and aggregating variables, i.e. the aggregation pathway. Although aggregation displacement and the type of aggregation pathway chosen for the indicator clearly impact both the indicator values and their interpretation, there are no clear guidelines or deliberation on these topics in the SEEA EA standard for ecosystem accounting. This paper outlines the consequences of different aggregation pathways, emphasising their impact on the credibility of ECAs, and how these are interpreted by users. We introduce a standardised terminology for aggregation pathways specific to ecosystem condition indicators following the SEEA EA standard and provide recommendations for selecting appropriate pathways in various contexts. Our discussion of this topic is aimed at raising the general awareness of spatial aggregation issues and to guide indicator developers in choosing and reporting spatial aggregation methods.

General ecosystem models (GEMs) are process‐based simulations of ecosystems functioning worldwide. They represent all essential processes in an ecosystem, going beyond a focus on specific biomes or organismal groups. GEMs are a comparably young development, and currently, there is still only one GEM available: the Madingley model, which was first announced in 2013. GEMs allow studying ecosystem mechanisms that are difficult or impossible to investigate empirically, and to perform simulation experiments to assess impacts of human pressures on ecosystems. As such, GEMs can be seen as the nature equivalent to ‘climate models'. As managers and policymakers urgently need good information on nature values and human impacts on them for policy design and decision‐making, they have much to gain from GEMs, which allow understanding current and predicting future outcomes of human actions on biodiversity and ecosystem functioning. To increase the applicability of GEMs for informing management and policy, we argue for an acceleration and diversification in model development, and we suggest a range of developments to propel GEMs forward not only towards both improved global representativeness but also towards applicability for more landscape‐level problems, a scale at which actual decision‐making often takes place. In addition, we argue for more accessible implementations and better documentation to increase both the transparency and credibility of GEMs, as well as the user basis applying GEMs. Practical implication. While some modelling developments target the global level, others are computationally only feasible for landscape‐level applications. We suggest that the development of GEMs should follow design principles employed in climate science and be structured so that they are modular and compatible in a standardized workflow.

Restoring degraded peatlands begins with hydrological restoration, but this alone may not fully re-establish key plant species such as Sphagnum mosses. To address the need for improving restoration outcomes, this study investigates the short-term effects of active revegetation on bare peat using a modified Moss Layer Transfer Technique (MLTT). Two treatment types were tested: Sphagnum fragments with mulch, mulch-only, and untreated bare peat as a control. Experiments were conducted across three locations in Norway to assess the recovery of Sphagnum cover and the establishment of other plant functional types over three years. Results showed a significant increase in Sphagnum cover in the Sphagnum treatment plots, reaching 65.6 % (± 3.5 SE) on average compared to 4.6 % (± 1.4 %) in mulch-only plots and 2.0 % (± 0.6 %) in controls, demonstrating the efficacy of active revegetation. The straw mulch treatment alone did not significantly enhance Sphagnum re-establishment, though some plant functional types, particularly mosses other than Sphagnum and Racomitrium as well as evergreen Ericaceae , benefited slightly. Sphagnum re-establishment success varied among the four species used, with S. angustifolium showing the highest establishment rates. Overall, our findings emphasise that active revegetation accelerates Sphagnum recovery, which is crucial for reducing greenhouse gas emissions and stabilising peatland hydrology. The use of straw mulch alone is of limited benefit for rapid Sphagnum recovery but may provide a stabilising microclimate. These results underline the need for integrating active revegetation with hydrological restoration for effective peatland restoration.

Ecosystem Condition Accounts (ECA) should reflect the integrity or quality of all nature inside the scope of the account, and therefore rely on spatially representative indicators for condition. All ECAs are subject to data constraints in some way. Therefore, being able to make use of spatially biased data sets would be very valuable. For national ECAs, modelling approaches can in some cases be used to control for sampling biases. For local ECAs however, like at the scale of individual municipalities of development projects, this is often not an option as it involves spatial extrapolation using data from outside the ecosystem accounting area. In this study we develop three ecosystem condition indicators from the same spatially biased data set based on nature type mapping of Norwegian mires. The indicators are Alien species, Trenching and Anthropogenic Disturbance to Soil and Vegetation. We test our approach in three municipalities in south Norway. We discretised a spatial variable representing infrastructure prevalence, and we refer to this new map as Homogeneous Impact Areas (HIAs). To facilitate reliable estimation of indicator uncertainties, even in cases with very low sample sizes, we use a Bayesian updating method and produce probability distributions for the area-weighted mean indicator values in each HIA separately. Then we use area-weighted resampling and produce indicator probability distributions for each municipality. With this Bayesian updating and resampling approach, small sample sizes can be compensated by correspondingly large uncertainty ranges, as long as the full dataset is large enough to estimate the true population standard deviation. This paper demonstrates the use of a GIS-based workflow to control for some of the most problematic biases in an opportunistic field survey so that the data can be used for indicators in ECAs. The workflow can be used at any scale, including national scale. Because indicator values are calculated for unique spatial strata, local governments or others can target their data acquisition towards strata with low sample sizes, and thus achieve higher cost effectiveness and ultimately better spatial indicator coverage.

The moose Alces alces is the largest herbivore in the boreal forest biome, where it can have dramatic impacts on ecosystem structure and dynamics. Despite the importance of the boreal forest biome in global carbon cycling, the impacts of moose have only been studied in disparate regional exclosure experiments, leading to calls for common analyses across a biome‐wide network of moose exclosures. In this study, we use airborne laser scanning (ALS) to analyse forest canopy responses to moose across 100 paired exclosure‐control experimental plots distributed across the boreal biome, including sites in the United States (Isle Royale), Canada (Quebec, Newfoundland), Norway, Sweden and Finland. We test the hypotheses that canopy height, vertical complexity and above‐ground biomass (AGB) are all reduced by moose and that the impacts vary with moose density, productivity, temperature and pulse disturbances such as logging and insect outbreaks. We find a surprising convergence in forest canopy response to moose. Moose had negative impacts on canopy height, complexity and AGB as expected. The responses of canopy complexity and AGB were consistent across regions and did not vary along environmental gradients. The difference in canopy height between exclosures and open plots was on average 6 cm per year since the start of exclosure treatment (±2.1 SD). This rate increased with temperature, but only when moose density was high. The difference in AGB between moose exclosures and open plots was 0.306 Mg ha⁻¹ year⁻¹ (±0.079). In browsed plots, stand AGB was 32% of that in the exclosures, a difference of 2.09 Mg ha⁻¹. The uniform response allows scaling of the estimate to a biome‐wide impact of moose of the loss of 448 (±115) Tg per year, or 224 Tg of carbon. Synthesis: Analysis of ALS data from distributed exclosure experiments identified a largely uniform response of forest canopies to moose across regions, facilitating scaling of moose impacts across the whole biome. This is an important step towards incorporating the effect of the largest boreal herbivore on the carbon cycling of one of the world's largest terrestrial biomes.

Background: Herbivores modify the structure and function of tundra ecosystems. Understanding their impacts is necessary to assess the responses of these ecosystems to ongoing environmental changes. However, the effects of herbivores on plants and ecosystem structure and function vary across the Arctic. Strong spatial variation in herbivore effects implies that the results of individual studies on herbivory depend on local conditions, i.e., their ecological context. An important first step in assessing whether generalizable conclusions can be produced is to identify the existing studies and assess how well they cover the underlying environmental conditions across the Arctic. This systematic map aims to identify the ecological contexts in which herbivore impacts on vegetation have been studied in the Arctic. Specifically, the primary question of the systematic map was: “What evidence exists on the effects of herbivores on Arctic vegetation?”. Methods: We used a published systematic map protocol to identify studies addressing the effects of herbivores on Arctic vegetation. We conducted searches for relevant literature in online databases, search engines and specialist websites. Literature was screened to identify eligible studies, defined as reporting primary data on herbivore impacts on Arctic plants and plant communities. We extracted information on variables that describe the ecological context of the studies, from the studies themselves and from geospatial data. We synthesized the findings narratively and created a Shiny App where the coded data are searchable and variables can be visually explored. Review findings: We identified 309 relevant articles with 662 studies (representing different ecological contexts or datasets within the same article). These studies addressed vertebrate herbivory seven times more often than invertebrate herbivory. Geographically, the largest cluster of studies was in Northern Fennoscandia. Warmer and wetter parts of the Arctic had the largest representation, as did coastal areas and areas where the increase in temperature has been moderate. In contrast, studies spanned the full range of ecological context variables describing Arctic vertebrate herbivore diversity and human population density and impact. Conclusions: The current evidence base might not be sufficient to understand the effects of herbivores on Arctic vegetation throughout the region, as we identified clear biases in the distribution of herbivore studies in the Arctic and a limited evidence base on invertebrate herbivory. In particular, the overrepresentation of studies in areas with moderate increases in temperature prevents robust generalizations about the effects of herbivores under different climatic scenarios.

Large herbivores are often classed as ecosystem engineers, and when they become scarce or overabundant, this can alter ecosystem states and influence climate forcing potentials. This realization has spurred a call to integrate large herbivores in earth system models. However, we lack a good understanding of their net effects on climate forcing, including carbon and energy exchange. A possible solution to this lies in harmonizing data across the myriad of large herbivore exclosure experiments around the world. This is challenging due to differences in experimental designs and field protocols. We used airborne laser scanning (ALS) to describe the effect of herbivore removal across 43 young boreal forest stands in Norway and found that exclusion caused the canopy height to increase from 1.7 ± 0.2 to 2.5 ± 0.2 m (means ± se), and also causing a marked increase in vertical complexity and above‐ground biomass. We then go on to discuss some of the issues with using ALS; we propose ALS as an approach for studying the effects of multiple large herbivore exclosure experiments simultaneously, and producing area‐based estimates on canopy structure and forest biomass in a cheap, efficient, standardized and reproducible way. We suggest that this is a vital next step towards generating biome‐wide predictions for the effects of large herbivores on forest ecosystem structure which can both inform both local management goals and earth system models.

Ungulate trampling modifies soils and interlinked ecosystem functions across biomes. Until today, most research has focused on temperate ecosystems and mineral soils while trampling effects on cold and organic matter‐rich tundra soils remain largely unknown. We aimed to develop a general model of trampling effects on soil structure, biota, microclimate and biogeochemical processes, with a particular focus on polar tundra soils. To reach this goal, we reviewed literature about the effects of trampling and physical disturbances on soils across biomes and used this to discuss the knowns and unknowns of trampling effects on tundra soils. We identified the following four pathways through which trampling affects soils: (a) soil compaction; (b) reductions in soil fauna and fungi; (c) rapid losses in vegetation biomass and cover; and (d) longer term shifts in vegetation community composition. We found that, in polar tundra, soil responses to trampling pathways 1 and 3 could be characterized by nonlinear dynamics and tundra‐specific context dependencies that we formulated into testable hypotheses. In conclusion, trampling may affect tundra soil significantly but many direct, interacting and cascading responses remain unknown. We call for research to advance the understanding of trampling effects on soils to support informed efforts to manage and predict the functioning of tundra systems under global changes. A free Plain Language Summary can be found within the Supporting Information of this article.