Snowmelt progression drives habitat selection and vegetation disturbance by an Arctic avian herbivore

Arctic tundra vegetation is affected by rapid climatic change and fluctuating herbivore population sizes. Broad-billed geese, after their arrival in spring, feed intensively on belowground rhizomes, thereby disturbing soil, mosses, and vascular plant vegetation. Understanding of how springtime snowmelt patterns drive goose behavior is thus key to better predict the state of Arctic tundra ecosystems. Here, we analyzed how snowmelt progression affected springtime habitat selection and vegetation disturbance by pink-footed geese (Anser brachyrhynchus) in Svalbard during 2019. Our analysis, based on GPS telemetry data and field observations of geese, plot-based assessments of signs of vegetation disturbance, and drone and satellite images, covered two spatial scales (fine scale: extent 0.3 km2, resolution 5 cm; valley scale: extent 30 km2, resolution 10 m). We show that pink-footed goose habitat selection and signs of vegetation disturbance were correlated during the spring pre-breeding period; disturbances were most prevalent in the moss tundra vegetation class and areas free from snow early in the season. The results were consistent across the spatial scales and methods (GPS telemetry and field observations). We estimated that 23.4% of moss tundra and 11.2% of dwarf-shrub heath vegetation in the valley showed signs of disturbance by pink-footed geese during the study period. This study demonstrates that aerial imagery and telemetry can provide data to detect disturbance hotspots caused by pink-footed geese. Our study provides empirical evidence to general notions about implications of climate change and snow season changes that include increased variability in precipitation.</p

MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY

Abstract. Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius

Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

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

Location of studies and evidence of effects of herbivory on Arctic vegetation : a systematic map

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

Developing common protocols to measure tundra herbivory across spatial scales

Understanding and predicting large-scale ecological responses to global environmental change requires comparative studies across geographic scales with coordinated efforts and standardized methodologies. We designed, applied, and assessed standardized protocols to measure tundra herbivory at three spatial scales: plot, site (habitat), and study area (landscape). The plot- and site-level protocols were tested in the field during summers 2014–2015 at 11 sites, nine of them consisting of warming experimental plots included in the International Tundra Experiment (ITEX). The study area protocols were assessed during 2014–2018 at 24 study areas across the Arctic. Our protocols provide comparable and easy to implement methods for assessing the intensity of invertebrate herbivory within ITEX plots and for characterizing vertebrate herbivore communities at larger spatial scales. We discuss methodological constraints and make recommendations for how these protocols can be used and how sampling effort can be optimized to obtain comparable estimates of herbivory, both at ITEX sites and at large landscape scales. The application of these protocols across the tundra biome will allow characterizing and comparing herbivore communities across tundra sites and at ecologically relevant spatial scales, providing an important step towards a better understanding of tundra ecosystem responses to large-scale environmental change

Machine learning for classification of an eroding scarp surface using terrestrial photogrammetry with nir and rgb imagery

Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius

Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs

High seasonal overlap in habitat suitability in a non-migratory High Arctic ungulate

Understanding drivers of space use and habitat selection is essential for management and conservation, especially under rapid environmental change. Here, we develop summer and winter habitat suitability models for the endemic wild Svalbard reindeer (Rangifer tarandus platyrhynchus). The High Arctic Svalbard tundra is currently subject to the fastest temperature increases on Earth, and reindeer spatial responses to associated environmental change are strongly restricted due to landscape barriers (including 60% glacial coverage) and lack of sea ice as movement corridors. We used an extensive dataset of GPS-collared adult females (2009–2018; N = 268 individual-years) to model seasonal habitat selection as a function of remotely sensed environmental variables , and subsequently built habitat suitability models using an ensemble modelling framework. As expected, we found that reindeer preferred productive habitats, described by the normalized difference vegetation index (NDVI) and plant biomass (derived from a vegetation map), in both seasons. This was further supported by selection for bird cliff areas, rich in forage, improving habitat suitability especially in winter. Contrary to our expectations, the terrain variables had similar, impact on habitat suitability in the two seasons, except for use of higher elevations in winter, likely related to improved forage access due to less snow. Suitable habitat patches covered only a small proportion of the landscape and were highly clustered in both seasons. About 13.0% of the total land area was suitable in both seasons, while summer-only and winter-only areas contributed a marginal addition of around 4.7% and 1.5%, respectively. This suggests, that unlike many continental and migratory Rangifer populations, even small geographic areas may encompass suffiscient suitable habitat. These first archipelago-wide habitat suitability models provide seasonal baseline maps relevant for the management and conservation of Svalbard reindeer, particularly under rapid environmental alterations from climate change