Craig E. Tweedie

Plant community responses to experimental warming across the tundra biome

Recent observations of changes in some tundra ecosystems appear to be responses to a warming climate. Several experimental studies have shown that tundra plants and ecosystems can respond strongly to environmental change, including warming; however, most studies were limited to a single location and were of short duration and based on a variety of experimental designs. In addition, comparisons among studies are difficult because a variety of techniques have been used to achieve experimental warming and different measurements have been used to assess responses. We used metaanalysis on plant community measurements from standardized warming experiments at 11 locations across the tundra biome involved in the International Tundra Experiment. The passive warming treatment increased plant-level air temperature by 1-3°C, which is in the range of predicted and observed warming for tundra regions. Responses were rapid and detected in whole plant communities after only two growing seasons. Overall, warming increased height and cover of deciduous shrubs and graminoids, decreased cover of mosses and lichens, and decreased species diversity and evenness. These results predict that warming will cause a decline in biodiversity across a wide variety of tundra, at least in the short term. They also provide rigorous experimental evidence that recently observed increases in shrub cover in many tundra regions are in response to climate warming. These changes have important implications for processes and interactions within tundra ecosystems and between tundra and the atmosphere.

Plot-scale evidence of tundra vegetation change and links to recent summer warming

Temperature is increasing at unprecedented rates across most of the tundra biome(1). Remote-sensing data indicate that contemporary climate warming has already resulted in increased productivity ov ...

Circumpolar Arctic Tundra Vegetation Change Is Linked to Sea Ice Decline

Abstract Linkages between diminishing Arctic sea ice and changes in Arctic terrestrial ecosystems have not been previously demonstrated. Here, the authors use a newly available Arctic Normalized Difference Vegetation Index (NDVI) dataset (a measure of vegetation photosynthetic capacity) to document coherent temporal relationships between near-coastal sea ice, summer tundra land surface temperatures, and vegetation productivity. The authors find that, during the period of satellite observations (1982–2008), sea ice within 50 km of the coast during the period of early summer ice breakup declined an average of 25% for the Arctic as a whole, with much larger changes in the East Siberian Sea to Chukchi Sea sectors (>44% decline). The changes in sea ice conditions are most directly relevant and have the strongest effect on the villages and ecosystems immediately adjacent to the coast, but the terrestrial effects of sea ice changes also extend far inland. Low-elevation (<300 m) tundra summer land temperatures, a...

TUNDRA CO2 FLUXES IN RESPONSE TO EXPERIMENTAL WARMING ACROSS LATITUDINAL AND MOISTURE GRADIENTS

Climate warming is expected to differentially affect CO2 exchange of the diverse ecosystems in the Arctic. Quantifying responses of CO2 exchange to warming in these ecosystems will require coordinated experimentation using standard temperature manipula- tions and measurements. Here, we used the International Tundra Experiment (ITEX) standard warming treatment to determine CO2 flux responses to growing-season warming for ecosystems spanning natural temperature and moisture ranges across the Arctic biome. We used the four North American Arctic ITEX sites (Toolik Lake, Atqasuk, and Barrow (USA) and Alexandra Fiord (Canada)) that span 108 of latitude. At each site, we investigated the CO2 responses to warming in both dry and wet or moist ecosystems. Net ecosystem CO2 exchange (NEE), ecosystem respiration (ER), and gross ecosystem photosynthesis (GEP) were assessed using chamber techniques conducted over 24-h periods sampled regularly throughout the summers of two years at all sites. At Toolik Lake, warming increased net CO2 losses in both moist and dry ecosystems. In contrast, at Atqasuk and Barrow, warming increased net CO2 uptake in wet ecosystems but increased losses from dry ecosystems. At Alexandra Fiord, warming improved net carbon uptake in the moist ecosystem in both years, but in the wet and dry ecosystems uptake increased in one year and decreased the other. Warming generally increased ER, with the largest increases in dry ecosystems. In wet ecosystems, high soil moisture limited increases in respiration relative to increases in photosynthesis. Warming generally increased GEP, with the notable exception of the Toolik Lake moist ecosystem, where warming unexpectedly decreased GEP .25%. Overall, the respiration response determined the effect of warming on ecosystem CO2 balance. Our results provide the first multiple-site comparison of arctic tundra CO2 flux responses to standard warming treatments across a large climate gradient. These results indicate that (1) dry tundra may be initially the most responsive ecosystems to climate warming by virtue of strong increases in ER, (2) moist and wet tundra responses are dampened by higher water tables and soil water contents, and (3) both GEP and ER are responsive to climate warming, but the magnitudes and directions are ecosystem-dependent.

Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets

The value of remote sensing and surface geophysical data for characterizing the spatial variability and relationships between land-surface and subsurface properties was explored in an Alaska (USA) coastal plain ecosystem. At this site, a nested suite of measurements was collected within a region where the land surface was dominated by polygons, including: LiDAR data; ground-penetrating radar, electromagnetic, and electrical-resistance tomography data; active-layer depth, soil temperature, soil-moisture content, soil texture, soil carbon and nitrogen content; and pore-fluid cations. LiDAR data were used to extract geomorphic metrics, which potentially indicate drainage potential. Geophysical data were used to characterize active-layer depth, soil-moisture content, and permafrost variability. Cluster analysis of the LiDAR and geophysical attributes revealed the presence of three spatial zones, which had unique distributions of geomorphic, hydrological, thermal, and geochemical properties. The correspondence between the LiDAR-based geomorphic zonation and the geophysics-based active-layer and permafrost zonation highlights the significant linkage between these ecosystem compartments. This study suggests the potential of combining LiDAR and surface geophysical measurements for providing high-resolution information about land-surface and subsurface properties as well as their spatial variations and linkages, all of which are important for quantifying terrestrial-ecosystem evolution and feedbacks to climate.ResuméLa portée de la télédétection et des données géophysique de surface pour caractériser la variabilité spatiale et les relations entre la surface du terrain et les propriétés de la subsurface a été étudiée sous tous ses aspects dans l’écosystème de la plaine côtière d’Alaska (USA). Dans cette région, sur un site où la surface du sol est dominée par des polygones, une série de données se recoupant a été collectée, incluant : données LiDAR; géoradar, tomographie électromagnétique et résistivité; profondeur de la couche aquifère, température, teneur en humidité, texture, teneur en carbone et en azote du sol; et cations du fluide des pores. Les données Lidar ont été utilisées pour établir les cotes géomorphiques, qui peuvent indiquer un drainage potentiel. Des données géophysiques ont été utilisées pour déterminer la profondeur de la couche aquifère, la teneur en humidité du sol, et la variabilité du pergélisol. L’analyse par agglomérat des données LiDAR et des attributs géophysiques ont révélé la présence de trois zones spatiales ayant une distribution similaire des propriétés géomorphiques, hydrogéologiques, thermales et géochimiques. La correspondance entre la zonation géomorphique basée sur LiDAR, la couche aquifère selon la géophysique et la zonation permafrost, met en lumière la relation significative entre ces compartiments de l’écosystème. Cette étude montre le potentiel d’une combinaison des mesures LiDAR et des mesures géophysiques de surface pour fournir une information haute résolution sur les propriétés de surface et de subsurface du sol aussi bien que sur leur variations spatiales et liens, toutes étant importantes pour quantifier l’évolution de l’écosystème terrestre et les réponses au climat.ResumenSe exploró el valor de los sensores remotos y de los datos geofísicos de superficie para caracterizar la variabilidad espacial y las relaciones entre la superficie y las propiedades subsuperficiales en un ecosistema de planicie costera en Alaska (EEUU). En este sitio, un conjunto anidado de medidas fue colectado dentro de una región donde la superficie estaba dominada por polígonos, incluyendo: datos LiDAR; datos de radar, electromagnéticos, y tomografías de resistividad eléctrica; profundidad de la capa activa, temperatura del suelo, contenido de humedad del suelo, textura del suelo, contenido de carbono y nitrógeno en suelo; y cationes del fluido de poros. Los datos LiDAR fueron usados para extraer los indicadores geomórficos, que posiblemente indican un drenaje potencial. Los datos geofísicos fueron para caracterizar la profundidad de la capa activa, el contenido de humedad del suelo y la variabilidad del permafrost. En análisis de cluster de los LiDAR y los atributos geofísicos revelaron la presencia espacial de tres zonas, que tenían una única distribución de propiedades geomórficas, hidrológicas, térmicas y geoquímicas. La correspondencia entre la zonación geomórfica basada en LiDAR y la capa activa basada en geofísica y la zonación del permafrost destaca la vinculación significativa entre estos compartimentos del ecosistema. Este estudio sugiere el potencial de la combinación LiDAR y las mediciones geofísicas de superficie para proveer información de alta resolución acerca de las propiedades de la superficie y de la subsuperficie así como su variación espacial y su articulación, todos los cuales son importantes para cuantificar la evolución del ecosistema terrestre y las reacciones con el clima.摘要用来描述地表和地下性质的空间变异性和两者之间的关系的遥感和地面地球物理数据已在阿拉斯加(美国)的一个沿海平原生态系统进行了探讨。在本次研究场地的一个表面呈多边形的区域收集到了一套测量数据,包括激光雷达数据;探地雷达数据,电磁和电阻断层扫描数据;活性层深度,土壤温度,土壤水气含量,土壤质地,土壤碳和氮的含量;以及孔隙流体阳离子数据。激光雷达数据用来提取地貌指标,这可能指示出潜在的排泄。地球物理数据用来刻画活性层的深度,土壤水气含量和永久冻土的变化特征。通过对激光雷达和地球物理数据属性的聚类分析发现了三个在地形,水文,热和地球化学性质分布上存在异常的空间区域。基于激光雷达测量的地貌分区与基于地球物理数据的活性层和永久冻土分区之间的对应关系突出了这些生态系统分区间的紧密联系。本次研究表明可以通过结合激光雷达和地表地球物理测量来为地表和地下的性质以及它们在空间上的变化和关系提供高分辨率的信息,所有这些对于量化陆地生态系统的演化和对气候变化的反应都是非常重要的。ResumoNum ecossistema da planície costeira do Alaska (EUA) foi explorado o valor da deteção remota e de dados de geofísica de superfície para caracterizar a variabilidade espacial e as relações entre propriedades da superfície do terreno e da subsuperfície. Neste local, inserido numa região onde o terreno superficial é dominado por polígonos, foi recolhido um conjunto agregado de medições, incluindo: dados de LiDAR; dados de geoadar, eletromagnéticos e de tomografia de resistência elétrica; profundidade da camada ativa, temperatura do solo, teor de água no solo, textura do solo, teor de carbono e azoto no solo; e catiões no fluido poroso. Os dados LiDAR foram usados para extrair dimensões geomórficas que potencialmente indicam o potencial de drenagem. Os dados geofísicos foram usados para caracterizar a profundidade da camada ativa, o teor de humidade no solo e a variabilidade no permafrost. A análise grupal de atributos LiDAR e geofísicos revelou a presença de três zonas espaciais que tinham distribuições únicas de propriedades geomórficas, hidrológicas, térmicas e geoquímicas. A correspondência entre o zonamento geomórfico baseado no LiDAR e a zonação da camada activa baseada na geofísica e do permafrost, demonstra a significativa conexão entre estes compartimentos do ecossistema. Este estudo sugere o potencial da combinação de medições de LiDAR e de geofísica de superfície para fornecer informação de alta resolução acerca das propriedades da superfície do terreno e da subsuperfície, assim como sobre as variações espaciais e conexões, sendo todas elas importantes para a quantificação da evolução do ecossistema terrestre e as retroações com o clima.

Multi-Decadal Changes in Tundra Environments and Ecosystems: Synthesis of the International Polar Year-Back to the Future Project (IPY-BTF)

Understanding the responses of tundra systems to global change has global implications. Most tundra regions lack sustained environmental monitoring and one of the only ways to document multi-decadal change is to resample historic research sites. The International Polar Year (IPY) provided a unique opportunity for such research through the Back to the Future (BTF) project (IPY project #512). This article synthesizes the results from 13 papers within this Ambio Special Issue. Abiotic changes include glacial recession in the Altai Mountains, Russia; increased snow depth and hardness, permafrost warming, and increased growing season length in sub-arctic Sweden; drying of ponds in Greenland; increased nutrient availability in Alaskan tundra ponds, and warming at most locations studied. Biotic changes ranged from relatively minor plant community change at two sites in Greenland to moderate change in the Yukon, and to dramatic increases in shrub and tree density on Herschel Island, and in sub-arctic Sweden. The population of geese tripled at one site in northeast Greenland where biomass in non-grazed plots doubled. A model parameterized using results from a BTF study forecasts substantial declines in all snowbeds and increases in shrub tundra on Niwot Ridge, Colorado over the next century. In general, results support and provide improved capacities for validating experimental manipulation, remote sensing, and modeling studies.

Experiment, monitoring, and gradient methods used to infer climate change effects on plant communities yield consistent patterns

Significance Methodological constraints can limit our ability to quantify potential impacts of climate warming. We assessed the consistency of three approaches in estimating warming effects on plant community composition: manipulative warming experiments, repeat sampling under ambient temperature change (monitoring), and space-for-time substitution. The three approaches showed agreement in the direction of change (an increase in the relative abundance of species with a warmer thermal niche), but differed in the magnitude of change estimated. Experimental and monitoring approaches were similar in magnitude, whereas space-for-time comparisons indicated a much stronger response. These results suggest that all three approaches are valid, but experimental warming and long-term monitoring are best suited for forecasting impacts over the coming decades. Inference about future climate change impacts typically relies on one of three approaches: manipulative experiments, historical comparisons (broadly defined to include monitoring the response to ambient climate fluctuations using repeat sampling of plots, dendroecology, and paleoecology techniques), and space-for-time substitutions derived from sampling along environmental gradients. Potential limitations of all three approaches are recognized. Here we address the congruence among these three main approaches by comparing the degree to which tundra plant community composition changes (i) in response to in situ experimental warming, (ii) with interannual variability in summer temperature within sites, and (iii) over spatial gradients in summer temperature. We analyzed changes in plant community composition from repeat sampling (85 plant communities in 28 regions) and experimental warming studies (28 experiments in 14 regions) throughout arctic and alpine North America and Europe. Increases in the relative abundance of species with a warmer thermal niche were observed in response to warmer summer temperatures using all three methods; however, effect sizes were greater over broad-scale spatial gradients relative to either temporal variability in summer temperature within a site or summer temperature increases induced by experimental warming. The effect sizes for change over time within a site and with experimental warming were nearly identical. These results support the view that inferences based on space-for-time substitution overestimate the magnitude of responses to contemporary climate warming, because spatial gradients reflect long-term processes. In contrast, in situ experimental warming and monitoring approaches yield consistent estimates of the magnitude of response of plant communities to climate warming.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r2 = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Tundra vegetation change near Barrow, Alaska (1972–2010)

Knowledge of how arctic plant communities will respond to change has been largely derived from plot level experimental manipulation, not from trends of decade time scale environmental observations. This study documents plant community change in 330 marked plots at 33 sites established during the International Biological Program near Barrow, Alaska in 1972. Plots were resampled in 1999, 2008 and 2010 for species cover and presence. Cluster analysis identified nine plant communities in 1972. Non-metric multidimensional scaling (NMS) indicates that plant communities have changed in different ways over time, and that wet communities have changed more than dry communities. The relative cover of lichens increased over time, while the response of other plant functional groups varied. Species richness and diversity also increased over time. The most dramatic changes in the cover of bryophytes, graminoids and bare ground coincided with a lemming high in 2008.

Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology

Most efforts to harness the power of big data for ecology and environmental sciences focus on data and metadata sharing, standardization, and accuracy. However, many scientists have not accepted the data deluge as an integral part of their research because the current scientific method is not scalable to large, complex datasets. Here, we explain how integrating a data-intensive, machine learning approach with a hypothesis-driven, mechanistic approach can lead to a novel knowledge, learning, analysis system (KLAS) for discovery and problem solving. Machine learning leads to more efficient, user-friendly analytics as the streams of data increase while hypothesis-driven decisions lead to the strategic design of experiments to fill knowledge gaps and to elucidate mechanisms. KLAS will transform ecology and environmental sciences by shortening the time lag between individual discoveries and leaps in knowledge by the scientific community, and will lead to paradigm shifts predicated on open access data and analytics in a machine learning environment.