Tropical ecosystems are under increasing pressure from land‐use change and deforestation. Changes in tropical forest cover are expected to affect carbon and water cycling with important implications for climatic stability at global scales. A major roadblock for predicting how tropical deforestation affects climate is the lack of baseline conditions (i.e., prior to human disturbance) of forest–savanna dynamics. To address this limitation, we developed a long‐term analysis of forest and savanna distribution across the Amazon–Cerrado transition of central Brazil. We used soil organic carbon isotope ratios as a proxy for changes in woody vegetation cover over time in response to fluctuations in precipitation inferred from speleothem oxygen and strontium stable isotope records. Based on stable isotope signatures and radiocarbon activity of organic matter in soil profiles, we quantified the magnitude and direction of changes in forest and savanna ecosystem cover. Using changes in tree cover measured in 83 different locations for forests and savannas, we developed interpolation maps to assess the coherence of regional changes in vegetation. Our analysis reveals a broad pattern of woody vegetation expansion into savannas and densification within forests and savannas for at least the past ~1,600 years. The rates of vegetation change varied significantly among sampling locations possibly due to variation in local environmental factors that constrain primary productivity. The few instances in which tree cover declined (7.7% of all sampled profiles) were associated with savannas under dry conditions. Our results suggest a regional increase in moisture and expansion of woody vegetation prior to modern deforestation, which could help inform conservation and management efforts for climate change mitigation. We discuss the possible mechanisms driving forest expansion and densification of savannas directly (i.e., increasing precipitation) and indirectly (e.g., decreasing disturbance) and suggest future research directions that have the potential to improve climate and ecosystem models.
This content will become publicly available on November 1, 2024
Alpine tundra ecosystems are highly vulnerable to climate warming but are governed by local‐scale abiotic heterogeneity, which makes it difficult to predict tundra responses to environmental change. Although land models are typically implemented at global scales, they can be applied at local scales to address process‐based ecological questions. In this study, we ran ecosystem‐scale Community Land Model (CLM) simulations with a novel hillslope hydrology configuration to represent topographically heterogeneous alpine tundra vegetation across a moisture gradient at Niwot Ridge, Colorado, USA. We used local observations to evaluate our simulations and investigated the role of topography and aspect in mediating patterns of snow, productivity, soil moisture, and soil temperature, as well as the potential exposure to climate change across an alpine tundra hillslope. Overall, our simulations captured observed gradients in abiotic conditions and productivity among heterogeneous, hydrologically connected vegetation communities (moist, wet, and dry). We found that south facing aspects were characterized by reduced snowpack and drier and warmer soils in all communities. When we extended our simulations to the year 2100, we found that earlier snowmelt altered the timing of runoff, with cascading effects on soil moisture, productivity, and growing season length. However, these effects were not distributed equally across the tundra, highlighting potential vulnerabilities of alpine vegetation in dry, wind‐scoured, and south facing areas. Overall, our results demonstrate how land model outputs can be applied to advance process‐based understanding of climate change impacts on ecosystem function.
more » « less- NSF-PAR ID:
- 10474064
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 128
- Issue:
- 11
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract We use a quantitative model of photosynthesis to explore leaf‐level limitations to plant growth in an alpine tundra ecosystem that is expected to have longer, warmer, and drier growing seasons. The model is parameterized with abiotic and leaf trait data that is characteristic of two dominant plant communities in the alpine tundra and specifically at the Niwot Ridge Long Term Ecological Research Site: the dry and wet meadows. Model results produce realistic estimates of photosynthesis, nitrogen‐use efficiency, water‐use efficiency, and other gas exchange processes in the alpine tundra. Model simulations suggest that dry and wet meadow plant species do not significantly respond to changes in the volumetric soil moisture content but are sensitive to variation in foliar nitrogen content. In addition, model simulations indicate that dry and wet meadow species have different maximum rates of assimilation (normalized for leaf nitrogen content) because of differences in leaf temperature. These differences arise from the interaction of plant height and the abiotic environment characteristic of each plant community. The leaf temperature of dry meadow species is higher than wet meadow species and close to the optimal temperature for photosynthesis under current conditions. As a result, 2°C higher air temperatures in the future will likely lead to declines in dry meadow species’ carbon assimilation. On the other hand, a longer and warmer growing season could increase nitrogen availability and assimilation rates in both plant communities. Nonetheless, a temperature increase of 4°C may lower rates of assimilation in both dry and wet meadow plant communities because of higher, and suboptimal, leaf temperatures.
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Abstract Aim How climate change will alter plant functional group composition is a critical question given the well‐recognized effects of plant functional groups on ecosystem services. While climate can have direct effects on different functional groups, indirect effects mediated through changes in biotic interactions have the potential to amplify or counteract direct climatic effects. As a result, identifying the underlying causes for climate effects on plant communities is important to conservation and restoration initiatives.
Location Western Pacific Northwest (Oregon and Washington), USA.
Methods Utilizing a 3‐year experiment in three prairie sites across a 520‐km latitudinal climate gradient, we manipulated temperature and precipitation and recorded plant cover at the peak of each growing season. We used structural equation models to examine how abiotic drivers (i.e. temperature, moisture and soil nitrogen) controlled functional group cover, and how these groups in turn determined overall plant diversity.
Results Warming increased the cover of introduced annual species, causing subsequent declines in other functional groups and diversity. While we found direct effects of temperature and moisture on extant vegetation (i.e. native annuals, native perennials and introduced perennials), these effects were typically amplified by introduced annuals. Competition for moisture and light or space, rather than nitrogen, were critical mechanisms of community change in this seasonally water‐limited Mediterranean‐climate system. Diversity declines were driven by reductions in native annual cover and increasing dominance by introduced annuals.
Main conclusions A shift towards increasing introduced annual dominance in this system may be akin to that previously experienced in California grasslands, resulting in the “Californication” of Pacific Northwest prairies. Such a phenomenon may challenge local land managers in their efforts to maintain species‐rich and functionally diverse prairie ecosystems in the future.
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As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.more » « less
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Abstract Global climate and land use change are causing woody plant encroachment in arctic, alpine, and arid/semi‐arid ecosystems around the world, yet our understanding of the belowground impacts of this phenomenon is limited. We conducted a globally distributed field study of 13 alpine sites across four continents undergoing woody plant encroachment and sampled soils from both woody encroached and nearby herbaceous plant community types. We found that woody plant encroachment influenced soil microbial richness and community composition across sites based on multiple factors including woody plant traits, site level climate, and abiotic soil conditions. In particular, root symbiont type was a key determinant of belowground effects, as Nitrogen‐fixing woody plants had higher soil fungal richness, while Ecto/Ericoid mycorrhizal species had higher soil bacterial richness and symbiont types had distinct soil microbial community composition. Woody plant leaf traits indirectly influenced soil microbes through their impact on soil abiotic conditions, primarily soil pH and C:N ratios. Finally, site‐level climate affected the overall magnitude and direction of woody plant influence, as soil fungal and bacterial richness were either higher or lower in woody encroached versus herbaceous soils depending on mean annual temperature and precipitation. All together, these results document global impacts of woody plant encroachment on soil microbial communities, but highlight that multiple biotic and abiotic pathways must be considered to scale up globally from site‐ and species‐level patterns. Considering both the aboveground and belowground effects of woody encroachment will be critical to predict future changes in alpine ecosystem structure and function and subsequent feedbacks to the global climate system.
