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Herbivores are an integral part of Arctic terrestrial ecosystems, driving ecosystem functioning and sustaining local livelihoods. In the context of accelerated climate warming and land use changes, understanding how herbivores contribute to the resilience of Arctic socio-ecological systems is essential to guide sound decision-making and mitigation strategies. While research on Arctic herbivory has a long tradition, recent literature syntheses highlight important geographical, taxonomic, and environmental knowledge gaps on the impacts of herbivores across the region. At the same time, climate change and limited resources impose an urgent need to prioritize research and management efforts. We conducted a horizon scan within the Arctic herbivory research community to identify emerging scientific and management priorities for the next decade. From 288 responses received from 85 participants in two online surveys and an in-person workshop, we identified 8 scientific and 8 management priorities centred on (a) understanding and integrating fundamental ecological processes across multiple scales from individual herbivore-plant interactions up to regional and decadal scale vegetation and animal population effects; (b) evaluating climate change feedbacks; and (c) developing new research methods. Our analysis provides a strategic framework for broad, inclusive, interdisciplinary collaborations to optimise terrestrial herbivory research and sustainable management practices in a rapidly changing Arctic.

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m−2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m−2 (mean ≈ 350 g m−2), while predicted values ranged from 0 to ∼4000 g m−2 (mean ≈ 275 g m−2), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m−2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

High-resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient-limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi-sensor data and deep learning for efficient ecological tracking and monitoring of mound-building ants and their spatial impact.

1. The term “applied ecology of fear” was recently introduced to describe the growing research field that applies the theory of the ecology of fear to manage wildlife behaviour. The management goal is to drive targeted species spatially and temporally away from areas of human interest by inducing cues from real or simulated predators to reduce human-wildlife conflict.

2. We aimed to quantify, through a meta-analysis, if prey anti-predator response would vary among field trials versus pen-based studies, predator cue types, predator hunting style and prey feeding type, and be stronger in response to larger predators relative to the prey's size. We also explored what studies found in terms of wildlife habituation to cues.

3. We used species belonging to the Cervidae family as a case study since deer are among the group of species with the highest degree of human-wildlife conflict. We retrieved 114 studies from online databases and collected information from 39 of those studies that fitted our research scope.

4. We found that acoustic cues more frequently led to an anti-predator response in deer than olfactory or visual cues. Neither predator hunting strategy nor deer feeding strategy or type of study (free-ranging or pen-based animals) influenced the extent to which deer responded to cues. Deer more frequently responded to cues that belonged to a larger predator relative to their size. Habituation was reported in less than one-third of the studies, with a study period ranging from 1 to 90 days, and occurred as soon as 7 days after the start of the study on average.

5. Our meta-analysis suggested that acoustic cues hold most potential as a tool to manage deer behaviour. These findings support the development of applied ecology of fear tools that introduce predator cues to reduce human-wildlife conflicts. Major knowledge gaps remain that limit the effective use of such tools in wildlife management and future research should focus on improving our understanding of habituation to cues, on comparing the effectiveness of different types of cues, on simultaneously using a combination of cue types, and on testing cues at spatial–temporal scales of actual land-uses.

Climate warming is degrading palsa peatlands across the circumpolar permafrost region. Permafrost degradation may lead to ecosystem collapse and potentially strong climate feedbacks, as this ecosystem is an important carbon store and can transition to being a strong greenhouse gas emitter. Landscape-level measurement of permafrost degradation is needed to monitor this impact of warming. Surface subsidence is a useful metric of change in palsa degradation and can be monitored using interferometric synthetic-aperture radar (InSAR) satellite technology. We combined InSAR data, processed using the ASPIS algorithm to monitor ground motion between 2017 and 2021, with airborne optical and lidar data to investigate the rate of subsidence across palsa peatlands in northern Sweden. We show that 55% of Sweden's eight largest palsa peatlands are currently subsiding, which can be attributed to the underlying permafrost landforms and their degradation. The most rapid degradation has occurred in the largest palsa complexes in the most northern part of the region of study, also corresponding to the areas with the highest percentage of palsa cover within the overall mapped wetland area. Further, higher degradation rates have been found in areas where winter precipitation has increased substantially. The roughness index calculated from a lidar-derived digital elevation model (DEM), used as a proxy for degradation, increases alongside subsidence rates and may be used as a complementary proxy for palsa degradation. We show that combining datasets captured using remote sensing enables regional-scale estimation of ongoing permafrost degradation, an important step towards estimating the future impact of climate change on permafrost-dependent ecosystems.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Global change is associated with variable shifts in the annual production of aboveground plant biomass, suggesting localized sensitivities with unclear causal origins. Combining remotely sensed normalized difference vegetation index data since the 1980s with contemporary field data from 84 grasslands on 6 continents, we show a widening divergence in site-level biomass ranging from +51% to −34% globally. Biomass generally increased in warmer, wetter and species-rich sites with longer growing seasons and declined in species-poor arid areas. Phenological changes were widespread, revealing substantive transitions in grassland seasonal cycling. Grazing, nitrogen deposition and plant invasion were prevalent in some regions but did not predict overall trends. Grasslands are undergoing sizable changes in production, with implications for food security, biodiversity and carbon storage especially in arid regions where declines are accelerating.

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1 in the top 30 cm and 231 ± 134 Mg SOC ha−1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.

Permafrost thaw in Arctic regions is increasing methane (CH₄) emissions into the atmosphere, but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth observation (EO) data are critical for assessing permafrost thaw, associated ecosystem change and increased CH₄ emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider. Landscape CH₄ emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH₄ emissions, and it is difficult to detect the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high-resolution unoccupied aerial vehicle (UAV) data and Sentinel-1 and Sentinel-2 data to extrapolate field measurements of CH₄ emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high-resolution UAV data can map spatial variation in vegetation relevant to variation in CH₄ emissions and extrapolate these across the wider landscape. We further show how this can be integrated with Sentinel-1 and Sentinel-2 data. By way of a soft classification and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH₄ emissions closely matched the results generated using the UAV data. Interferometric synthetic-aperture radar (InSAR) assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH₄ emissions. The transition of a 50 ha area currently experiencing subsidence to fen vegetation is estimated to increase emissions from 116 kg CH₄ per season to emissions as high as 6500 to 13 000 kg CH₄ per season. The key outcome from this study is that a combination of high- and low-resolution EO data of different types provides the ability to estimate CH₄ emissions from large geographies covered by a fine mixture of vegetation types which are vulnerable to transitioning to CH₄ emitters in the near future. This points to an opportunity to measure and monitor CH₄ emissions from the Arctic over space and time with confidence.

Ungulates play an important role in temperate systems. Through their feeding behaviour, they can respond to vegetation by selecting patches or modify vegetation composition by herbivory. The degree in which they interact with vegetation can either reinforce landscape heterogeneity by creating disturbance or reduce heterogeneity in case of overbrowsing. This study evaluates how bottom-up (patch quality, structure), top-down forces (hunting, distance to village, forest edge) and deer features (feeding type, abundance) mediate patch utilization in a temperate forest and assess the implications of patch utilization and light on forest recruitment. Theory predicts that animals seek to maximize their energetic gains by food intake while minimizing the costs associated to foraging, such as the energy required for avoiding predators and exploiting resources. We focused on two deer species with contrasting feeding type: a browser (C. capreolus) and a mixed feeder (C. elaphus). We paired camera traps to vegetation sub-plots in ten forest sites in the Netherlands that widely ranged in deer abundance and landscape heterogeneity. Results showed that patch utilization is simultaneously explained by bottom-up, top-down forces and by deer abundance, as predicted by the safety-in-numbers hypothesis. Yet, forces best explaining patch utilization differed between deer species. Overall, higher patch utilization came with higher browsing, lower tree diversity and a large difference in forest composition: from a mix of broadleaves and conifers towards only conifers. We conclude that these two deer species, although living in the same area and belonging to the same guild, differentially perceive, interact with and shape their surrounding landscape.