Search for: All records

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 presentThe 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.

 

Changes in vegetation distribution are underway in Arctic and boreal regions due to climate warming and associated fire disturbance. These changes have wide ranging downstream impacts—affecting wildlife habitat, nutrient cycling, climate feedbacks and fire regimes. It is thus critical to understand where these changes are occurring and what types of vegetation are affected, and to quantify the magnitude of the changes. In this study, we mapped live aboveground biomass for five common plant functional types (PFTs; deciduous shrubs, evergreen shrubs, forbs, graminoids and lichens) within Alaska and northwest Canada, every five years from 1985 to 2020. We employed a multi-scale approach, scaling from field harvest data and unmanned aerial vehicle-based biomass predictions to produce wall-to-wall maps based on climatological, topographic, phenological and Landsat spectral predictors. We found deciduous shrub and graminoid biomass were predicted best among PFTs. Our time-series analyses show increases in deciduous (37%) and evergreen shrub (7%) biomass, and decreases in graminoid (14%) and lichen (13%) biomass over a study area of approximately 500 000 km². Fire was an important driver of recent changes in the study area, with the largest changes in biomass associated with historic fire perimeters. Decreases in lichen and graminoid biomass often corresponded with increasing shrub biomass. These findings illustrate the driving trends in vegetation change within the Arctic/boreal region. Understanding these changes and the impacts they in turn will have on Arctic and boreal ecosystems will be critical to understanding the trajectory of climate change in the region.

 

Competition for resources and space can drive forage selection of large herbivores from the bite through the landscape scale. Animal behaviour and foraging patterns are also influenced by abiotic and biotic factors. Fine‐scale mechanisms of density‐dependent foraging at the bite scale are likely consistent with density‐dependent behavioural patterns observed at broader scales, but few studies have directly tested this assertion. Here, we tested if space use intensity, a proxy of spatiotemporal density, affects foraging mechanisms at fine spatial scales similarly to density‐dependent effects observed at broader scales in caribou. We specifically assessed how behavioural choices are affected by space use intensity and environmental processes using behavioural state and forage selection data from caribou (Rangifer tarandus granti) observed from GPS video‐camera collars using a multivariate discrete‐choice modelling framework. We found that the probability of eating shrubs increased with increasing caribou space use intensity and cover of Salix spp. shrubs, whereas the probability of eating lichen decreased. Insects also affected fine‐scale foraging behaviour by reducing the overall probability of eating. Strong eastward winds mitigated negative effects of insects and resulted in higher probabilities of eating lichen. At last, caribou exhibited foraging functional responses wherein their probability of selecting each food type increased as the availability (% cover) of that food increased. Space use intensity signals of fine‐scale foraging were consistent with density‐dependent responses observed at larger scales and with recent evidence suggesting declining reproductive rates in the same caribou population. Our results highlight potential risks of overgrazing on sensitive forage species such as lichen. Remote investigation of the functional responses of foraging behaviours provides exciting future applications where spatial models can identify high‐quality habitats for conservation. 

Arctic landscapes are rapidly changing with climate warming. Vegetation communities are restructuring, which in turn impacts wildlife, permafrost, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but notable mismatches in scale occur between current field and satellite-based monitoring. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite imagery mapping. In this work we assess the viability of using high resolution UAV imagery (RGB and multispectral), along with UAV derived Structure from Motion (SfM) to predict cover, height and above-ground biomass of common Arctic plant functional types (PFTs) across a wide range of vegetation community types. We collected field data and UAV imagery from 45 sites across Alaska and northwest Canada. We then classified UAV imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Here we present datasets summarizing this data. 

Seasonal migrations are a widespread and broadly successful strategy for animals to exploit periodic and localized resources over large spatial scales. It remains an open and largely case-specific question whether long-distance migrations are resilient to environmental disruptions. High levels of mobility suggest an ability to shift ranges that can confer resilience. On the other hand, a conservative, hard-wired commitment to a risky behavior can be costly if conditions change. Mechanisms that contribute to migration include identification and responsiveness to resources, sociality, and cognitive processes such as spatial memory and learning. Our goal was to explore the extent to which these factors interact not only to maintain a migratory behavior but also to provide resilience against environmental changes. We develop a diffusion-advection model of animal movement in which an endogenous migratory behavior is modified by recent experiences via a memory process, and animals have a social swarming-like behavior over a range of spatial scales. We found that this relatively simple framework was able to adapt to a stable, seasonal resource dynamic under a broad range of parameter values. Furthermore, the model was able to acquire an adaptive migration behavior with time. However, the resilience of the process depended on all the parameters under consideration, with many complex trade-offs. For example, the spatial scale of sociality needed to be large enough to capture changes in the resource, but not so large that the acquired collective information was overly diluted. A long-term reference memory was important for hedging against a highly stochastic process, but a higher weighting of more recent memory was needed for adapting to directional changes in resource phenology. Our model provides a general and versatile framework for exploring the interaction of memory, movement, social and resource dynamics, even as environmental conditions globally are undergoing rapid change.