Vegetation phenology and productivity play a crucial role in surface energy balance, plant and animal distribution, and animal movement and habitat use and can be measured with remote sensing metrics including start of season (SOS), peak instantaneous rate of green-up date (PIRGd), peak of season (POS), end of season (EOS), and integrated vegetation indices. However, for most metrics, we do not yet understand the agreement of remotely sensed data products with near-surface observations. We also need summaries of changes over time, spatial distribution, variability, and consistency in remote sensing dataset metrics for vegetation timing and quality. We compare metrics from 10 leading remote sensing datasets against a network of PhenoCam near-surface cameras throughout the western United States from 2002 to 2014. Most phenology metrics representing a date (SOS, PIRGd, POS, and EOS), rather than a duration (length of spring, length of growing season), better agreed with near-surface metrics but results varied by dataset, metric, and land cover, with absolute value of mean bias ranging from 0.38 (PIRGd) to 37.92 days (EOS). Datasets had higher agreement with PhenoCam metrics in shrublands, grasslands, and deciduous forests than in evergreen forests. Phenology metrics had higher agreement than productivity metrics, aside from a few datasets in deciduous forests. Using two datasets covering the period 1982–2016 that best agreed with PhenoCam metrics, we analyzed changes over time to growing seasons. Both datasets exhibited substantial spatial heterogeneity in the direction of phenology trends. Variability of metrics increased over time in some areas, particularly in the Southwest. Approximately 60% of pixels had consistent trend direction between datasets for SOS, POS, and EOS, with the direction varying by location. In all ecoregions except Mediterranean California, EOS has become later. This study comprehensively compares remote sensing datasets across multiple growing season metrics and discusses considerations for applied users to inform their data choices.
Snow dynamics influence seasonal behaviors of wildlife, such as denning patterns and habitat selection related to the availability of food resources. Under a changing climate, characteristics of the temporal and spatial patterns of snow are predicted to change, and as a result, there is a need to better understand how species interact with snow dynamics. This study examines grizzly bear ( Ursus arctos ) spring habitat selection and use across western Alberta, Canada. Made possible by newly available fine-scale snow cover data, this research tests a hypothesis that grizzly bears select for locations with less snow cover and areas where snow melts sooner during spring (den emergence to May 31 st ). Using Integrated Step Selection Analysis, a series of models were built to examine whether snow cover information such as fractional snow covered area and date of snow melt improved models constructed based on previous knowledge of grizzly bear selection during the spring. Comparing four different models fit to 62 individual bear-years, we found that the inclusion of fractional snow covered area improved model fit 60% of the time based on Akaike Information Criterion tallies. Probability of use was then used to evaluate grizzly bear habitat use in response to snow and environmental attributes, including fractional snow covered area, date since snow melt, elevation, and distance to road. Results indicate grizzly bears select for lower elevation, snow-free locations during spring, which has important implications for management of threatened grizzly bear populations in consideration of changing climatic conditions. This study is an example of how fine spatial and temporal scale remote sensing data can be used to improve our understanding of wildlife habitat selection and use in relation to key environmental attributes.
Wildlife aggregation patterns can influence disease transmission. However, limited research evaluates the influence of anthropogenic and natural factors on aggregation. Many managers would like to reduce wildlife contact rates, driven by aggregation, to limit disease transmission. We develop a novel analytical framework to quantify how management activities such as supplemental feeding and hunting versus weather drive contact rates while accounting for correlated contacts. We apply the framework to the National Elk Refuge (NER), Wyoming, USA, where the probable arrival of chronic wasting disease (CWD) has magnified concerns. We used a daily proximity index to measure contact rates among 68 global positioning system collared elk from 2016 to 2019. We modelled contact rates as a function of abiotic weather‐related effects, anthropogenic effects and aggregation from the prior day. The winter of 2017–2018 had greater natural forage availability and little snow, which led to a rare non‐feeding year on the NER and provided a unique opportunity to evaluate the effect of feeding on contact rates relative to other conditions. Supplemental feeding was the strongest predictor of aggregation, and contact rates were 2.6 times larger while feeding occurred compared to the baseline rate (0.34 and 0.13, respectively). Snow‐covered area was the second strongest predictor of contact rates highlighting the importance of abiotic factors to elk aggregation, but this effect had half the strength of feeding. These results are the first to show, even in animals that congregate naturally, how greatly supplemental feeding amplifies aggregation. Contact rates were also 23% lower during times when elk hunting was active (0.10) compared to the baseline. Synthesis and applications. Supplemental feeding increased contacts between elk well above the natural effects of weather, even after accounting for correlated movement expected in wintering ungulates. Similarly, differences in hunting season timing with adjacent areas led to an increase in contacts, suggesting an additional management option for reducing aggregation. The analytical framework presented supports the evaluation of temporally varying management actions that influence aggregation broadly and can be easily implemented whether the interest in changing aggregation is related to reduction of disease transmission, human–wildlife conflict or inter‐species competition.
Aims To model regional vegetation cycles through data fusion methods for creating a 30‐m daily vegetation product from 2000 to 2018 and to analyze annual vegetation trends over this time period. Location The Yellowhead Bear Management Area, a 31,180‐km2 area in west central Alberta, Canada. Methods In this paper, we use Dynamic Time Warping (DTW) as a data fusion technique to combine Landsat 5, 7 and 8 satellite data and Moderate Resolution Image Spectroradiometer (MODIS) Aqua and Terra imagery, to quantify daily vegetation using Enhanced Vegetation Index at a 30‐m resolution, for the years 2000–2018. We validated this approach, entitled DRIVE (Daily Remote Inference of VEgetation), using imagery acquired from a network of ground cameras. Results When DRIVE was compared to start and end of season dates (SOS and EOS respectively) derived from ground cameras, correlations were r = 0.73 at SOS and r = 0.85 at EOS with a mean absolute error of 7.17 days at SOS and 10.76 days at EOS. Results showed that DRIVE accurately increased spatial and temporal resolution of remote‐sensing data. We demonstrated that SOS is advancing at a maximum rate of 0.78 days per year temporally over the 18‐year time period for varying elevation gradients and land cover classes over the region. Conclusions With DRIVE, we demonstrate the utility of DTW in quantifying vegetation cycles over a large heterogeneous region and determining how changing climate is affecting regional vegetation. DRIVE may prove to be an important method to determine how carbon sequestration is varying within fine‐scale individual plant communities in response to changing climate and likely will be beneficial to wildlife movement and habitat selection studies examining the varying response of wildlife species to changing vegetation cycles under shifting climatic conditions.
Changing environmental conditions are altering how animals interact with their habitats (Schmitz, Post, Burns, & Johnston, 2003). In many cases, directional changes in environmental conditions owing to changing climatic conditions are resulting in altered habitats in space and time which in turn affect animal behavior
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