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Images from time-lapse cameras were analyzed to track the greenness curves of 16 plots in the Sensor Network at Niwot Ridge. Images were taken every 30 minutes during daylight hours throughout the growing season. Cameras were angled to view 1m^2 vegetation plots located at each sensor node. Pixels in the portion of the image capturing the vegetation plot were used to calculate the green chromatic coordinate (GCC). The change in GCC over the growing season represents the growth and phenology of the plant communities captured. 

Abstract Climate‐induced shifts in mosquito phenology and population structure have important implications for the health of humans and wildlife. The timing and intensity of mosquito interactions with infected and susceptible hosts are a primary determinant of vector‐borne disease dynamics. Like most ectotherms, rates of mosquito development and corresponding phenological patterns are expected to change under shifting climates. However, developing accurate forecasts of mosquito phenology under climate change that can be used to inform management programs remains challenging despite an abundance of available data. As climate change will have variable effects on mosquito demography and phenology across species it is vital that we identify associated traits that may explain the observed variation. Here, we review a suite of modeling approaches that could be applied to generate forecasts of mosquito activity under climate change and evaluate the strengths and weaknesses of the different approaches. We describe four primary life history and physiological traits that can be used to constrain models and demonstrate how this prior information can be harnessed to develop a more general understanding of how mosquito activity will shift under changing climates. Combining a trait‐based approach with appropriate modeling techniques can allow for the development of actionable, flexible, and multi‐scale forecasts of mosquito population dynamics and phenology for diverse stakeholders. 

Abstract Tundra plants are widely considered to be constrained by cool growing conditions and short growing seasons. Furthermore, phenological development is generally predicted by daily heat sums calculated as growing degree days. Analyzing over a decade of seasonal flower counts of 23 plant species distributed across four plant communities, together with hourly canopy-temperature records, we show that the timing of flowering of many tundra plants are best predicted by a modified growing degree day model with a maximum temperature threshold. Threshold maximums are commonly employed in agriculture, but until recently have not been considered for natural ecosystems and to our knowledge have not been used for tundra plants. Estimated maximum temperature thresholds were found to be within the range of daily temperatures commonly experienced for many species, particularly for plants at the colder, high Arctic study site. These findings provide an explanation for why passive experimental warming—where moderate changes in mean daily temperatures are accompanied by larger changes in daily maximum temperatures—generally shifts plant phenology less than ambient warming. Our results also suggest that many plants adapted to extreme cold environments may have limits to their thermal responsiveness. 

The below-ground growing season often extends beyond the above-ground growing season in tundra ecosystems. However, we do not yet know where and when this occurs and whether these phenological asynchronies are driven by variation in local vegetation communities or by spatial variation in microclimate. Here, we combined above- and below-ground plant phenology metrics to compare the relative timings and magnitudes of leaf and root growth and senescence across microclimates and plant communities at five sites across the tundra biome. We observed asynchronous growth between above-ground and below-ground plant tissue, with the below-ground season extending up to 74% beyond the onset of above-ground leaf senescence. Plant community type, rather than microclimate, was a key factor controlling the timing, productivity and growth rates of roots, with graminoid roots exhibiting a distinct ‘pulse’ of growth later into the growing season than shrub roots. Our findings indicate the potential of vegetation change to influence below-ground carbon storage as roots remain active in unfrozen soils for longer as the climate warms. Taken together, increased root growth in soils that remain thawed later into the growing season, in combination with ongoing tundra vegetation change including increased shrubs and graminoids, can act together to alter below-ground productivity and carbon cycling in the tundra biome. 

Abstract The Arctic is warming four times faster than the global average¹and plant communities are responding through shifts in species abundance, composition and distribution^2–4. However, the direction and magnitude of local changes in plant diversity in the Arctic have not been quantified. Using a compilation of 42,234 records of 490 vascular plant species from 2,174 plots across the Arctic, here we quantified temporal changes in species richness and composition through repeat surveys between 1981 and 2022. We also identified the geographical, climatic and biotic drivers behind these changes. We found greater species richness at lower latitudes and warmer sites, but no indication that, on average, species richness had changed directionally over time. However, species turnover was widespread, with 59% of plots gaining and/or losing species. Proportions of species gains and losses were greater where temperatures had increased the most. Shrub expansion, particularly of erect shrubs, was associated with greater species losses and decreasing species richness. Despite changes in plant composition, Arctic plant communities did not become more similar to each other, suggesting no biotic homogenization so far. Overall, Arctic plant communities changed in richness and composition in different directions, with temperature and plant–plant interactions emerging as the main drivers of change. Our findings demonstrate how climate and biotic drivers can act in concert to alter plant composition, which could precede future biodiversity changes that are likely to affect ecosystem function, wildlife habitats and the livelihoods of Arctic peoples^5,6. 

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover. 

Open top chambers (OTCs) were adopted as the recommended warming mechanism by the International Tundra Experiment (ITEX) network in the early 1990’s. Since then, OTCs have been deployed across the globe. Hundreds of papers have reported the impacts of OTCs on the abiotic environment and the biota. Here we review the impacts of the OTC on the physical environment, with comments on the appropriateness of using OTCs to characterize the response of biota to warming. The purpose of this review is to guide readers to previously published work and to provide recommendations for continued use of OTCs to understand the implications of warming on low stature ecosystems. In short, the OTC is a useful tool to experimentally manipulate temperature, however the characteristics and magnitude of warming varies greatly in different environments, therefore it is important to document chamber performance to maximize the interpretation of biotic response. When coupled with long-term monitoring, warming experiments are a valuable means to understand the impacts of climate change on natural ecosystems. 

The research data repository of the Environmental Data Initiative (EDI) is building on over 30 years of data curation research and experience in the National Science Foundation-funded US Long-Term Ecological Research (LTER) Network. It provides mature functionalities, well established workflows, and now publishes all ‘long-tail’ environmental data. High quality scientific metadata are enforced through automatic checks against community developed rules and the Ecological Metadata Language (EML) standard. Although the EDI repository is far along in making its data findable, accessible, interoperable, and reusable (FAIR), representatives from EDI and the LTER are developing best practices for the edge cases in environmental data publishing. One of these is the vast amount of imagery taken in the context of ecological research, ranging from wildlife camera traps to plankton imaging systems to aerial photography. Many images are used in biodiversity research for community analyses (e.g., individual counts, species cover, biovolume, productivity), while others are taken to study animal behavior and landscape-level change. Some examples from the LTER Network include: using photos of a heron colony to measure provisioning rates for chicks (Clarkson and Erwin 2018) or identifying changes in plant cover and functional type through time (Peters et al. 2020). Multi-spectral images are employed to identify prairie species. Underwater photo quads are used to monitor changes in benthic biodiversity (Edmunds 2015). Sosik et al. (2020) used a continuous Imaging FlowCytobot to identify and measure phyto- and microzooplankton. Cameras at McMurdo Dry Valleys assess snow and ice cover on Antarctic lakes allowing estimation of primary production (Myers 2019). It has been standard practice to publish numerical data extracted from images in EDI; however, the supporting imagery generally has not been made publicly available. Our goal in developing best practices for documenting and archiving these images is for them to be discovered and re-used. Our examples demonstrate several issues. The research questions, and hence, the image subjects are variable. Images frequently come in logical sets of time series. The size of such sets can be large and only some images may be contributed to a dedicated specialized repository. Finally, these images are taken in a larger monitoring context where many other environmental data are collected at the same time and location. Currently, a typical approach to publishing image data in EDI are packages containing compressed (ZIP or tar) files with the images, a directory manifest with additional image-specific metadata, and a package-level EML metadata file. Images in the compressed archive may be organized within directories with filenames corresponding to treatments, locations, time periods, individuals, or other grouping attributes. Additionally, the directory manifest table has columns for each attribute. Package-level metadata include standard coverage elements (e.g., date, time, location) and sampling methods. This approach of archiving logical ‘sets’ of images reduces the effort of providing metadata for each image when most information would be repeated, but at the expense of not making every image individually searchable. The latter may be overcome if the provided manifest contains standard metadata that would allow searching and automatic integration with other images.