Observed and predicted effects of climate change on Arctic caribou and reindeer

Mallory, Conor D.; Boyce, Mark S.

doi:10.1139/er-2017-0032

Cited by 112 publications

(133 citation statements)

References 240 publications

(398 reference statements)

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“…Post, Forchhammer, et al () pointed out that spatial environmental heterogeneity and scale‐dependent ecological responses are key to understanding and predicting net implications of the various climate change effects reported in Arctic tundra study sites. In caribou and reindeer, some observed population declines have indeed been linked to global environmental change, but the proposed drivers seem to differ locally and include habitat fragmentation (Festa‐Bianchet, Ray, Boutin, Côté, & Gunn, ), Arctic ‘shrubification’ (Fauchald, Park, Tømmervik, Myneni, & Hausner, ), increased insect harassment (Mallory & Boyce, ) and worsening winter‐feeding conditions (Forbes et al, ; Hansen et al, ). Also, the drivers of change may vary in time.…”

Section: Discussionmentioning

confidence: 99%

Spatial heterogeneity in climate change effects decouples the long‐term dynamics of wild reindeer populations in the high Arctic

Hansen

Pedersen

Peeters

et al. 2019

Global Change Biology

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The ‘Moran effect’ predicts that dynamics of populations of a species are synchronized over similar distances as their environmental drivers. Strong population synchrony reduces species viability, but spatial heterogeneity in density dependence, the environment, or its ecological responses may decouple dynamics in space, preventing extinctions. How such heterogeneity buffers impacts of global change on large‐scale population dynamics is not well studied. Here, we show that spatially autocorrelated fluctuations in annual winter weather synchronize wild reindeer dynamics across high‐Arctic Svalbard, while, paradoxically, spatial variation in winter climate trends contribute to diverging local population trajectories. Warmer summers have improved the carrying capacity and apparently led to increased total reindeer abundance. However, fluctuations in population size seem mainly driven by negative effects of stochastic winter rain‐on‐snow (ROS) events causing icing, with strongest effects at high densities. Count data for 10 reindeer populations 8–324 km apart suggested that density‐dependent ROS effects contributed to synchrony in population dynamics, mainly through spatially autocorrelated mortality. By comparing one coastal and one ‘continental’ reindeer population over four decades, we show that locally contrasting abundance trends can arise from spatial differences in climate change and responses to weather. The coastal population experienced a larger increase in ROS, and a stronger density‐dependent ROS effect on population growth rates, than the continental population. In contrast, the latter experienced stronger summer warming and showed the strongest positive response to summer temperatures. Accordingly, contrasting net effects of a recent climate regime shift—with increased ROS and harsher winters, yet higher summer temperatures and improved carrying capacity—led to negative and positive abundance trends in the coastal and continental population respectively. Thus, synchronized population fluctuations by climatic drivers can be buffered by spatial heterogeneity in the same drivers, as well as in the ecological responses, averaging out climate change effects at larger spatial scales.

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Section: Discussionmentioning

confidence: 99%

Spatial heterogeneity in climate change effects decouples the long‐term dynamics of wild reindeer populations in the high Arctic

Hansen

Pedersen

Peeters

et al. 2019

Global Change Biology

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“…Investigators should recognize that forage-NDVI relationships are temporally dynamic, and thus, that NDVI may represent different aspects of forage conditions at different times of the growing season. Given uncertainties about the influence of climate change and plant phenology on caribou forage conditions (Mallory and Boyce 2017), and the implications for caribou population dynamics, additional field studies to elucidate these relationships are imperative. As a result, we encourage practitioners to carefully employ NDVI metrics in studies of ungulate ecology, working to validate assumptions with field data on forage conditions in different study systems.…”

Section: Discussionmentioning

confidence: 99%

NDVI exhibits mixed success in predicting spatiotemporal variation in caribou summer forage quality and quantity

Johnson

Gustine²,

Golden

et al. 2018

Ecosphere

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The satellite-derived Normalized Difference Vegetation Index (NDVI) is commonly used by researchers and managers to represent ungulate forage conditions in landscapes across the globe, despite limited information about how it compares to empirical measurements of forage quality and quantity. The application of NDVI as a forage metric is particularly appealing for studying migratory caribou (Rangifer tarandus) in remote Arctic ecosystems, where field assessments are logistically and financially prohibitive, and climate-mediated changes in vegetation have been hypothesized to influence population declines. To determine the utility of NDVI for adequately representing caribou forage conditions, we compared NDVI derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery to empirical measures of caribou forage biomass, nitrogen, digestible nitrogen, and digestible energy within the summer range of the Central Arctic Caribou Herd on the North Slope of Alaska. Specifically, we determined the strength of forage-NDVI relationships at the start of the growing season and across the summer, assessed the efficacy of NDVI variables for modeling spatiotemporal variation in field measurements of different forage components, and used long-term MODIS data to estimate temporal changes in forage between 2000 and 2016. We found that NDVI values were weakly correlated with caribou forage quality at the start of the growing season and throughout the summer. Although linear models of forage-NDVI relationships performed poorly, NDVI variables (NDVI and the number of days from when NDVI reached its maximum value) were useful for modeling spatiotemporal variation in empirical measurements of forage components across the growing season, but only when we incorporated nonlinear forage-NDVI relationships and other habitat covariates. Phenological advances in the date of peak NDVI were associated with significant changes in forage conditions, particularly nitrogen, which exhibited earlier seasonal declines. Using long-term MODIS data, predicted values of forage nitrogen declined between 2000 and 2016, driven by exceedingly low values in 2014 and 2015. Given our results, we caution the application of NDVI as a general (linear) proxy of caribou forage conditions across the growing season, and encourage practitioners to use NDVI variables to model spatiotemporal variation in specific forage conditions from empirical field data, accounting for nonlinear forage-NDVI relationships.

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“…The Arctic is warming at twice the rate of lower latitudes and is predicted to experience elevated levels of precipitation with a higher proportion falling as rain (Olsen et al 2011, Bintanja and Selten 2014, Bring et al 2016, Vihma et al 2016. As a consequence, the snow-covered season is shortening, spring conditions are becoming more variable, and rain-on-snow events are increasing in frequency (Putkonen et al 2009, Jeong and Sushama 2017, Langlois et al 2017, Mallory and Boyce 2017. These changes in temperature and hydrology are transforming northern ecosystems, with profound implications for wildlife that are not well understood , 2011b, Chapin et al 2004, Post et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

Mahoney

Liston

LaPoint

et al. 2018

Ecological Applications

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Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100 kg/m ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300 kg/m ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties.

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Observed and predicted effects of climate change on Arctic caribou and reindeer

Cited by 112 publications

References 240 publications

Spatial heterogeneity in climate change effects decouples the long‐term dynamics of wild reindeer populations in the high Arctic

Spatial heterogeneity in climate change effects decouples the long‐term dynamics of wild reindeer populations in the high Arctic

NDVI exhibits mixed success in predicting spatiotemporal variation in caribou summer forage quality and quantity

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

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