Search

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 habitua

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.

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.

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 habitua

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.

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 habitua

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.

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., 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.

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., 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.

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.

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 habitua

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.

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 tha, Errata: https://doi.org/10.5194/bg-20-4221-2023-corrigendum

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 tha, Errata: https://doi.org/10.5194/bg-20-4221-2023-corrigendum

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.

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.

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.

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.

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 tha, Errata: https://doi.org/10.5194/bg-20-4221-2023-corrigendum

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 tha, Errata: https://doi.org/10.5194/bg-20-4221-2023-corrigendum

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.

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.

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.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season's start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover's role for vegetation, plant- animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season's start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover's role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22 % of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9×106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300 m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0–100 and 0–300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (−170 to +186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database reposito

Global change affects gross primary production (GPP) in benthic and pelagic habitats of northern lakes by influencing catchment characteristics and lake water biogeochemistry. However, how changes in key environmental drivers manifest and impact total (i.e., benthic + pelagic) GPP and the partitioning of total GPP between habitats represented by the benthic share (autotrophic structuring) is unclear. Using a dataset from 26 shallow lakes located across Arctic, subarctic, and boreal northern Sweden, we investigate how catchment properties (air temperature, land cover, hydrology) affect lake physico-chemistry and patterns of total GPP and autotrophic structuring. We find that total GPP was mostly light limited, due to high dissolved organic carbon (DOC) concentrations originating from catchment soils with coniferous vegetation and wetlands, which is further promoted by high catchment runoff. In contrast, autotrophic structuring related mostly to the relative size of the benthic habitat, and was potentially modified by CO2 fertilization in the subarctic, resulting in significantly higher total GPP relative to the other biomes. Across Arctic and subarctic sites, DIC and CO2 were unrelated to DOC, indicating that external inputs of inorganic carbon can influence lake productivity patterns independent of terrestrial DOC supply. By comparison, DOC and CO2 were correlated across boreal lakes, suggesting that DOC mineralization acts as an important CO2 source for these sites. Our results underline that GPP as a resource is regulated by landscape properties, and is sensitive to large-scale global changes (warming, hydrological intensification, recovery of acidification) that promote changes in catchment characteristics and aquatic physico-chemistry. Our findings aid in predicting global change impacts on autotrophic structuring, and thus community structure and resource use of aquatic consumers in general. Given the similarities of global changes across the Northern hemispher, Originally included in thesis in manuscript form.

Global change affects gross primary production (GPP) in benthic and pelagic habitats of northern lakes by influencing catchment characteristics and lake water biogeochemistry. However, how changes in key environmental drivers manifest and impact total (i.e., benthic + pelagic) GPP and the partitioning of total GPP between habitats represented by the benthic share (autotrophic structuring) is unclear. Using a dataset from 26 shallow lakes located across Arctic, subarctic, and boreal northern Sweden, we investigate how catchment properties (air temperature, land cover, hydrology) affect lake physico-chemistry and patterns of total GPP and autotrophic structuring. We find that total GPP was mostly light limited, due to high dissolved organic carbon (DOC) concentrations originating from catchment soils with coniferous vegetation and wetlands, which is further promoted by high catchment runoff. In contrast, autotrophic structuring related mostly to the relative size of the benthic habitat, and was potentially modified by CO2 fertilization in the subarctic, resulting in significantly higher total GPP relative to the other biomes. Across Arctic and subarctic sites, DIC and CO2 were unrelated to DOC, indicating that external inputs of inorganic carbon can influence lake productivity patterns independent of terrestrial DOC supply. By comparison, DOC and CO2 were correlated across boreal lakes, suggesting that DOC mineralization acts as an important CO2 source for these sites. Our results underline that GPP as a resource is regulated by landscape properties, and is sensitive to large-scale global changes (warming, hydrological intensification, recovery of acidification) that promote changes in catchment characteristics and aquatic physico-chemistry. Our findings aid in predicting global change impacts on autotrophic structuring, and thus community structure and resource use of aquatic consumers in general. Given the similarities of global changes across the Northern hemispher, Originally included in thesis in manuscript form.

Global change affects gross primary production (GPP) in benthic and pelagic habitats of northern lakes by influencing catchment characteristics and lake water biogeochemistry. However, how changes in key environmental drivers manifest and impact total (i.e., benthic + pelagic) GPP and the partitioning of total GPP between habitats represented by the benthic share (autotrophic structuring) is unclear. Using a dataset from 26 shallow lakes located across Arctic, subarctic, and boreal northern Sweden, we investigate how catchment properties (air temperature, land cover, hydrology) affect lake physico-chemistry and patterns of total GPP and autotrophic structuring. We find that total GPP was mostly light limited, due to high dissolved organic carbon (DOC) concentrations originating from catchment soils with coniferous vegetation and wetlands, which is further promoted by high catchment runoff. In contrast, autotrophic structuring related mostly to the relative size of the benthic habitat, and was potentially modified by CO2 fertilization in the subarctic, resulting in significantly higher total GPP relative to the other biomes. Across Arctic and subarctic sites, DIC and CO2 were unrelated to DOC, indicating that external inputs of inorganic carbon can influence lake productivity patterns independent of terrestrial DOC supply. By comparison, DOC and CO2 were correlated across boreal lakes, suggesting that DOC mineralization acts as an important CO2 source for these sites. Our results underline that GPP as a resource is regulated by landscape properties, and is sensitive to large-scale global changes (warming, hydrological intensification, recovery of acidification) that promote changes in catchment characteristics and aquatic physico-chemistry. Our findings aid in predicting global change impacts on autotrophic structuring, and thus community structure and resource use of aquatic consumers in general. Given the similarities of global changes across the Northern hemispher, Originally included in thesis in manuscript form.

Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maxi-mum of −25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed sub-sidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands., This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands.

Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maxi-mum of −25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed sub-sidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands., This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands.

Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maxi-mum of −25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed sub-sidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands., This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22% of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9 x 106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0-100 and 0-300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (170 to C186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repositor

Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maxi-mum of −25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed sub-sidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands., This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands.

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22% of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9 x 106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0-100 and 0-300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (170 to C186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repositor

Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maxi-mum of −25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed sub-sidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands., This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands.

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22% of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9 x 106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0-100 and 0-300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (170 to C186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repositor

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season's start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover's role for vegetation, plant- animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season's start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover's role for vegetation, plant- animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season's start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover's role for vegetation, plant- animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22% of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9 x 106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0-100 and 0-300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (170 to C186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repositor

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22% of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9 x 106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0-100 and 0-300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (170 to C186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repositor

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.