Quantifying snow controls on vegetation greenness

No effect of snow on shrub xylem traits: Insights from a snow-manipulation experiment on Disko Island, Greenland

Widespread shrubification across the Arctic has been generally attributed to increasing air temperatures, but responses vary across species and sites. Wood structures related to the plant hydraulic architecture may respond to local environmental conditions and potentially impact shrub growth, but these relationships remain understudied. Using methods of dendroanatomy, we analysed shrub ring width (RW) and xylem anatomical traits of 80 individuals of Salix glauca L. and Betula nana L. at a snow manipulation experiment in Western Greenland. We assessed how their responses differed between treatments (increased versus ambient snow depth) and soil moisture regimes (wet and dry). Despite an increase in snow depth due to snow fences (28-39 %), neither RW nor anatomical traits in either species showed significant responses to this increase. In contrast, irrespective of the snow treatment, the xylem specific hydraulic conductivity (Ks) and earlywood vessel size (LA95) for the study period were larger in S. glauca (p < 0.1, p < 0.01) and B. nana (p < 0.01, p < 0.001) at the wet than the dry site, while both species had larger vessel groups at the dry than the wet site (p < 0.01). RW of B. nana was higher at the wet site (p < 0.01), but no differences were observed for S. glauca. Additionally, B. nana Ks and LA95 showed different trends over the study period, with decreases observed at the dry site (p < 0.001), while for other responses no difference was observed. Our results indicate that, taking into account ontogenetic and allometric trends, hydraulic related xylem traits of both species, along with B. nana growth, were influenced by soil moisture. These findings suggest that soil moisture regime, but not snow cover, may determine xylem responses to future climate change and thus add to the heterogeneity of Arctic shrub dynamics, though more long-term species- and site- specific studies are needed.

Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland

Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum normalized difference vegetation index (NDVImax ) day, end of fall) for five dominating land surface classes in the ice-free Greenland. Using a distributed snow model, structural equation modeling, and a random forest model, based on ground observations and remote sensing data, we assessed the indirect and direct effects of climate, snow, and terrain on seasonal transition dates. We then presented new projections of likely changes in seasonal transition dates under six future climate scenarios. The coupled climate, snow cover, and terrain conditions explained up to 61% of seasonal transition dates across different land surface classes. Snow ending day played a crucial role in the start of spring and timing of NDVImax . A warmer June and a decline in wind could advance the NDVImax day. Increased precipitation and temperature during July-August are the most important for delaying the end of fall. We projected that a 1-4.5°C increase in temperature and a 5%-20% increase in precipitation would lengthen the spring-to-fall period for all five land surface classes by 2050, thus the current order of spring-to-fall lengths for the five land surface classes could undergo notable changes. Tall shrubs and fens would have a longer spring-to-fall period under the warmest and wettest scenario, suggesting a competitive advantage for these vegetation communities. This study's results illustrate controls on seasonal transition dates and portend potential changes in vegetation composition in the Arctic under climate change.

Accounting for behaviour in fine-scale habitat selection: A case study highlighting methodological intricacies

Animal habitat selection-central in both theoretical and applied ecology-may depend on behavioural motivations such as foraging, predator avoidance, and thermoregulation. Step-selection functions (SSFs) enable assessment of fine-scale habitat selection as a function of an animal's movement capacities and spatiotemporal variation in extrinsic conditions. If animal location data can be associated with behaviour, SSFs are an intuitive approach to quantify behaviour-specific habitat selection. Fitting SSFs separately for distinct behavioural states helped to uncover state-specific selection patterns. However, while the definition of the availability domain has been highlighted as the most critical aspect of SSFs, the influence of accounting for behaviour in the use-availability design has not been quantified yet. Using a predator-free population of high-arctic muskoxen Ovibos moschatus as a case study, we aimed to evaluate how (1) defining behaviour-specific availability domains, and/or (2) fitting separate behaviour-specific models impacts (a) model structure, (b) estimated selection coefficients and (c) model predictive performance as opposed to behaviour-unspecific approaches. To do so, we first applied hidden Markov models to infer different behavioural modes (resting, foraging, relocating) from hourly GPS positions (19 individuals, 153-1062 observation days/animal). Using SSFs, we then compared behaviour-specific versus behaviour-unspecific habitat selection in relation to terrain features, vegetation and snow conditions. Our results show that incorporating behaviour into the definition of the availability domain primarily impacts model structure (i.e. variable selection), whereas fitting separate behaviour-specific models mainly influences selection strength. Behaviour-specific availability domains improved predictive performance for foraging and relocating models (i.e. behaviours with medium to large spatial displacement), but decreased performance for resting models. Thus, even for a predator-free population subject to only negligible interspecific competition and human disturbance we found that accounting for behaviour in SSFs impacted model structure, selection coefficients and predictive performance. Our results indicate that for robust inference, both a behaviour-specific availability domain and behaviour-specific model fitting should be explored, especially for populations where strong spatiotemporal selection trade-offs are expected. This is particularly critical if wildlife habitat preferences are estimated to inform management and conservation initiatives.

Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types

In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.

Carbon response of tundra ecosystems to advancing greenup and snowmelt in Alaska

The ongoing disproportionate increases in temperature and precipitation over the Arctic region may greatly alter the latitudinal gradients in greenup and snowmelt timings as well as associated carbon dynamics of tundra ecosystems. Here we use remotely-sensed and ground-based datasets and model results embedding snowmelt timing in phenology at seven tundra flux tower sites in Alaska during 2001-2018, showing that the carbon response to early greenup or delayed snowmelt varies greatly depending upon local climatic limits. Increases in net ecosystem productivity (NEP) due to early greenup were amplified at the higher latitudes where temperature and water strongly colimit vegetation growth, while NEP decreases due to delayed snowmelt were alleviated by a relief of water stress. Given the high likelihood of more frequent delayed snowmelt at higher latitudes, this study highlights the importance of understanding the role of snowmelt timing in vegetation growth and terrestrial carbon cycles across warming Arctic ecosystems.

Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.

Quantifying effects of snow depth on caribou winter range selection and movement in Arctic Alaska

BACKGROUND

Caribou and reindeer across the Arctic spend more than two thirds of their lives moving in snow. Yet snow-specific mechanisms driving their winter ecology and potentially influencing herd health and movement patterns are not well known. Integrative research coupling snow and wildlife sciences using observations, models, and wildlife tracking technologies can help fill this knowledge void.

METHODS

Here, we quantified the effects of snow depth on caribou winter range selection and movement. We used location data of Central Arctic Herd (CAH) caribou in Arctic Alaska collected from 2014 to 2020 and spatially distributed and temporally evolving snow depth data produced by SnowModel. These landscape-scale (90 m), daily snow depth data reproduced the observed spatial snow-depth variability across typical areal extents occupied by a wintering caribou during a 24-h period.

RESULTS

We found that fall snow depths encountered by the herd north of the Brooks Range exerted a strong influence on selection of two distinct winter range locations. In winters with relatively shallow fall snow depth (2016/17, 2018/19, and 2019/20), the majority of the CAH wintered on the tundra north of the Brooks Range mountains. In contrast, during the winters with relatively deep fall snow depth (2014/15, 2015/16, and 2017/18), the majority of the CAH caribou wintered in the mountainous boreal forest south of the Brooks Range. Long-term (19 winters; 2001-2020) monitoring of CAH caribou winter distributions confirmed this relationship. Additionally, snow depth affected movement and selection differently within these two habitats: in the mountainous boreal forest, caribou avoided areas with deeper snow, but when on the tundra, snow depth did not trigger significant deep-snow avoidance. In both wintering habitats, CAH caribou selected areas with higher lichen abundance, and they moved significantly slower when encountering deeper snow.

CONCLUSIONS

In general, our findings indicate that regional-scale selection of winter range is influenced by snow depth at or prior to fall migration. During winter, daily decision-making within the winter range is driven largely by snow depth. This integrative approach of coupling snow and wildlife observations with snow-evolution and caribou-movement modeling to quantify the multi-facetted effects of snow on wildlife ecology is applicable to caribou and reindeer herds throughout the Arctic.

Environment and physiology shape Arctic ungulate population dynamics

Species conservation in a rapidly changing world requires an improved understanding of how individuals and populations respond to changes in their environment across temporal scales. Increased warming in the Arctic puts this region at particular risk for rapid environmental change, with potentially devastating impacts on resident populations. Here, we make use of a parameterized full life cycle, individual-based energy budget model for wild muskoxen, coupling year-round environmental data with detailed ontogenic metabolic physiology. We show how winter food accessibility, summer food availability, and density dependence drive seasonal dynamics of energy storage and thus life history and population dynamics. Winter forage accessibility defined by snow depth, more than summer forage availability, was the primary determinant of muskox population dynamics through impacts on calf recruitment and longer term carryover effects of maternal investment. Simulations of various seasonal snow depth and plant biomass and quality profiles revealed that timing of and improved/limited winter forage accessibility had marked influence on calf recruitment (±10-80%). Impacts on recruitment were the cumulative result of condition-driven reproductive performance at multiple time points across the reproductive period (ovulation to calf weaning) as a trade-off between survival and reproduction. Seasonal and generational condition effects of snow-rich winters interacted with age structure and density to cause pronounced long-term consequences on population growth and structure, with predicted population recovery times from even moderate disturbances of 10 years or more. Our results show how alteration in winter forage accessibility, mediated by snow depth, impacts the dynamics of northern herbivore populations. Further, we present here a mechanistic and state-based model framework to assess future scenarios of environmental change, such as increased or decreased snowfall or plant biomass and quality to impact winter and summer forage availability across the Arctic.

Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities

Tundra dominates two-thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community-specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil.

Quantifying energetic and fitness consequences of seasonal heterothermy in an Arctic ungulate

Animals have adapted behavioral and physiological strategies to conserve energy during periods of adverse conditions. Heterothermy is one such adaptation used by endotherms. While heterothermy-fluctuations in body temperature and metabolic rate-has been shown in large vertebrates, little is known of the costs and benefits of this strategy, both in terms of energy and in terms of fitness. Hence, our objective was to model the energetics of seasonal heterothermy in the largest Arctic ungulate, the muskox (Ovibos moschatus), using an individual-based energy budget model of metabolic physiology. We found that the empirically based drop in body temperature (winter max ~-0.8°C) overwinter in adult females resulted in substantial fitness benefits in terms of reduced daily energy expenditure and body mass loss. Body mass and energy reserves were 8.98% and 14.46% greater in modeled heterotherms compared to normotherms by end of winter. Based on environmental simulations, we show that seasonal heterothermy can, to some extent, buffer the negative consequences of poor prewinter body condition or reduced winter food accessibility, leading to greater winter survival (+20%-30%) and spring energy reserves (+10%-30%), and thus increased probability of future reproductive success. These results indicate substantial adaptive short-term benefits of seasonal heterothermy at the individual level, with potential implications for long-term population dynamics in highly seasonal environments.

A Lagrangian Snow-Evolution System for Sea-Ice Applications (SnowModel-LG): Part I-Model Description

A Lagrangian snow-evolution model (SnowModel-LG) was used to produce daily, pan-Arctic, snow-on-sea-ice, snow property distributions on a 25 × 25-km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective-Analysis for Research and Applications-Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis-5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14-km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static-surfaces and blowing-snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing-snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt-season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA-2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first-order control on snow property evolution.

Environmental conditions alter behavioural organization and rhythmicity of a large Arctic ruminant across the annual cycle

The existence and persistence of rhythmicity in animal activity during phases of environmental change is of interest in ecology, evolution and chronobiology. A wide diversity of biological rhythms in response to exogenous conditions and internal stimuli have been uncovered, especially for polar vertebrates. However, empirical data supporting circadian organization in behaviour of large ruminating herbivores remains inconclusive. Using year-round tracking data of the largest Arctic ruminant, the muskox (Ovibos moschatus), we modelled rhythmicity as a function of behaviour and environmental conditions. Behavioural states were classified based on patterns in hourly movements, and incorporated within a periodicity analyses framework. Although circadian rhythmicity in muskox behaviour was detected throughout the year, ultradian rhythmicity was most prevalent, especially when muskoxen were foraging and resting in mid-winter (continuous darkness). However, when combining circadian and ultradian rhythmicity together, the probability of behavioural rhythmicity declined with increasing photoperiod until largely disrupted in mid-summer (continuous light). Individuals that remained behaviourally rhythmic during mid-summer foraged in areas with lower plant productivity (NDVI) than individuals with arrhythmic behaviour. Based on our study, we conclude that muskoxen may use an interval timer to schedule their behavioural cycles when forage resources are low, but that the importance and duration of this timer are reduced once environmental conditions allow energetic reserves to be replenished ad libitum. We argue that alimentary function and metabolic requirements are critical determinants of biological rhythmicity in muskoxen, which probably applies to ruminating herbivores in general.

An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore

BACKGROUND

In highly seasonal environments, animals face critical decisions regarding time allocation, diet optimisation, and habitat use. In the Arctic, the short summers are crucial for replenishing body reserves, while low food availability and increased energetic demands characterise the long winters (9-10 months). Under such extreme seasonal variability, even small deviations from optimal time allocation can markedly impact individuals' condition, reproductive success and survival. We investigated which environmental conditions influenced daily, seasonal, and interannual variation in time allocation in high-arctic muskoxen (Ovibos moschatus) and evaluated whether results support qualitative predictions derived from upscaled optimal foraging theory.

METHODS

Using hidden Markov models (HMMs), we inferred behavioural states (foraging, resting, relocating) from hourly positions of GPS-collared females tracked in northeast Greenland (28 muskox-years). To relate behavioural variation to environmental conditions, we considered a wide range of spatially and/or temporally explicit covariates in the HMMs.

RESULTS

While we found little interannual variation, daily and seasonal time allocation varied markedly. Scheduling of daily activities was distinct throughout the year except for the period of continuous daylight. During summer, muskoxen spent about 69% of time foraging and 19% resting, without environmental constraints on foraging activity. During winter, time spent foraging decreased to 45%, whereas about 43% of time was spent resting, mediated by longer resting bouts than during summer.

CONCLUSIONS

Our results clearly indicate that female muskoxen follow an energy intake maximisation strategy during the arctic summer. During winter, our results were not easily reconcilable with just one dominant foraging strategy. The overall reduction in activity likely reflects higher time requirements for rumination in response to the reduction of forage quality (supporting an energy intake maximisation strategy). However, deep snow and low temperatures were apparent constraints to winter foraging, hence also suggesting attempts to conserve energy (net energy maximisation strategy). Our approach provides new insights into the year-round behavioural strategies of the largest Arctic herbivore and outlines a practical example of how to approximate qualitative predictions of upscaled optimal foraging theory using multi-year GPS tracking data.

Cross-ecosystem nutrient subsidies in Arctic and alpine lakes: implications of global change for remote lakes

Environmental change is continuing to affect the flow of nutrients, material and organisms across ecosystem boundaries. These cross-system flows are termed ecosystem subsidies. Here, we synthesize current knowledge of cross-ecosystem nutrient subsidies between remote lakes and their surrounding terrain, cryosphere, and atmosphere. Remote Arctic and alpine lakes are ideal systems to study the effects of cross ecosystem subsidies because (a) they are positioned in locations experiencing rapid environmental changes, (b) they are ecologically sensitive to even small subsidy changes, (c) they have easily defined ecosystem boundaries, and (d) a variety of standard methods exist that allow for quantification of lake subsidies and their impacts on ecological communities and ecosystem functions. We highlight similarities and differences between Arctic and alpine systems and identify current knowledge gaps to be addressed with future work. It is important to understand the dynamics of nutrient and material flows between lakes and their environments in order to improve our ability to predict ecosystem responses to continued environmental change.

Density, snow, and seasonality lead to variation in muskox (Ovibos moschatus) habitat selection during summer

Understanding how environmental conditions influence habitat selection and suitability of free-ranging animals is critical, as the outcome may have implications for individual fitness and population dynamics. Density and snow are among the most influential environmental conditions driving habitat-selection patterns of northern ungulates. We used two decades of census data from high Arctic Greenland to quantify inter- and intra-annual variations in muskox (Ovibos moschatus (Zimmermann, 1780)) habitat selection and suitability during the Arctic summer (July through October). Across years, habitat selection varied considerably, and the strength of habitat selection appeared negatively related to both muskox density and spring snow cover. In early summer, habitat suitability was high and spatially rather uniform. Towards the autumn, suitable habitats contracted to just the lower elevations, when muskoxen exhibited increasingly stronger habitat selection towards low elevations and dense vegetation. This selection strategy clearly reflects the need to build up fat reserves for the upcoming winter, highlighting the energetic importance of the Arctic summer. Extreme climatic events such as freezing rain in autumn are increasing in frequency in Greenland and limit muskox access to high-quality forage in fens. Such events may therefore negatively affect the energy acquisition process of muskox with potential cascading consequences on population dynamics.