Multiple Biomes Research Articles

Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data

Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration's (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale. We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000–2004 and 2005–2006, respectively. We found that the model predicted NEE well ( r = 0.73, p < 0.001). We then applied the model to the continental scale and estimated NEE for each 1 km × 1 km cell across the conterminous U.S. for each 8-day interval in 2005 using spatially explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE as determined from measurements and the literature. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets over large areas.

The so-called “Paleophytic–Mesophytic” transition in equatorial Pangea — Multiple biomes and vegetational tracking of climate change through geological time

The Paleophytic, Mesophytic and Cenophytic were originally conceptualized as chronostratigraphic intervals (not specific floras) and were used principally in that way for about 100 years. They supposedly reflected massive, irreversible biotic changes on land that were thought to have happened about half a geological period prior to the changes in marine faunas that mark the more traditional geological era boundaries. The terms were applied initially only to terrestrial rocks from the Euramerican portions of equatorial Pangea, especially parts of central Europe, where the boundary between the Paleophytic and Mesophytic, the “eras” of interest here, was placed at the top of the German Rotliegends. More recently, the terms have been coopted to describe vegetation types (not chronostratigraphic units), even to the point where specific Linnean ordinal or class level clades (evolutionary lineages) of plants have been attributed solely to one vegetation type or the other at a global level. Furthermore, the “Paleophytic–Mesophytic” transition has been recognized in the largely taxonomically distinct vegetation types of temperate as well as the equatorial paleolatitudes. The result of this conceptual coopting of terms that already were of questionable value, is a blurring of the concepts almost to the point of meaninglessness. A reading of the literature indicates confusion in terms of what represents the “Paleophytic” flora vs. the “Mesophytic” flora, the botanical basis for the floras and the taxonomic scale at which they can be recognized, the geographic pattern and temporal correlation of the supposed transition, and the evolutionary and ecological significance of the vegetational changes these terms supposedly capture. We propose that these terms be completely and utterly abandoned as confusing and, worse yet, misleading. Evidence suggests that Paleozoic vegetation tracked climate, which should not come as a surprise in light of what is known of plant dynamics in the Holocene and Recent. Furthermore, the evidence for a global “Paleophytic” vs. “Mesophytic” “vegetation” is simply unsubstantiated by the fossil record. Rather, there appears to have been a complex of global species pools reflecting climate at many spatio-temporal scales. The vegetational changes occurring in the late Paleozoic thus can be understood best when examined as spatial–temporal changes in biome-scale species pools responding to major global climate changes, locally and regionally manifested.

Global Ocean Sampling Collection

Today, PLoS Biology publishes landmark metagenomics papers from the J. Craig Venter Institute's Global Ocean Sampling expedition [1–3]. These papers describe the initial analyses of several gigabasepairs' worth of sequence data from oceanic microbes collected during the Sorcerer II expedition, as the ship made her way down from Canada, through the Panama Canal, and finally out beyond the Galapagos Islands well into the tropical Pacific and the South Pacific Gyre. Results from the first foray of this research mission into the Sargasso Sea were published three years ago [4]. As described in the accompanying Synopsis [5], the new voyage has added information from multiple biomes and several-fold more data. Analysis of these data poses not only scientific challenges [6], but also significant legal hurdles. Craig Venter is no stranger to issues of intellectual property—his previous incarnation as the president of Celera saw him embroiled in controversy over the decision to “privatize” aspects of his company's work in sequencing the human genome. Now, at the head of the Global Ocean Sampling project, Venter finds himself on the side of greater accessibility, negotiating the claims of individual governments on the genomic wealth within their waters. In particular, as of this writing, there is an active negotiation with the Ecuadorian government (which has seen more than one change of power since the expedition began) over restricting commercial reuse of these data. Henry Nicholls describes this tangled legal landscape in an accompanying Feature [7]. Although extensive in scope, the papers presented here only touch the surface of the wealth of information to be gleaned from these data, which are freely available for all to explore from their desktops: the trace reads and processed data have been deposited in the National Center for Biotechnology Information's Trace Archive (http://www.ncbi.nlm.nih.gov/Traces) (with the exception of that fraction of the trace data acquired from Ecuadorian coastal waters), annotated with extensive geographical and physicochemical metadata. The assemblies and associated annotated peptides will be delivered to GenBank (http://www.ncbi.nlm.nih.gov/Genbank) around the time of publication, and will become available after GenBank has processed them. More immediately, and potentially more usefully, these data are also freely available through a specially built database, CAMERA—Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (http://camera.calit2.net)—which provides greater annotation and analysis capabilities [8]. (CAMERA was funded by the Gordon and Betty Moore Foundation, which also supports PLoS.) The proponents of open-access publishing, ourselves included, often cite as an inspiration the power that open access to DNA sequence databases has had in transforming scientific discovery. As our founders noted in the inaugural issue of PLoS Biology, “With great foresight, it was decided in the early 1980s that published DNA sequences should be deposited in a central repository, in a common format, where they could be freely accessed and used by anyone. Simply giving scientists free and unrestricted access to the raw sequences led them to develop the powerful methods, tools, and resources that have made the whole much greater than the sum of the individual sequences…. Now imagine the possibilities if the same creative explosion that was fueled by open access to DNA sequences were to occur for the much larger body of published scientific results.” [9] But the publishing reality in genomics research has been less inspiring. Although sequence data are publicly available and free to be reused by the community, the same creative license has not yet been awarded to the key papers resulting from the major genome projects, which are commonly published in subscription-based journals. Many of these genomics papers are “freely” available from publisher Web sites, but their use remains restricted, and to claim that freedom to read an article is the main benefit of open access is to miss the promise inspired by DNA sequence databases. While we and other open-access journals have both enjoyed and been grateful for strong support from the genomics community, we are also disappointed that authors of landmark genomics papers, who adamantly support open access to sequence data, have not taken the opportunity to provide further leadership for their community by promoting open access to the scientific literature. We encourage all researchers to apply the same standards to their papers as they would to their data, regardless of the publisher. As Jensen et al. stated in a recent review about the benefits of text mining for the scientific community, “It is the restricted access to the full text of papers…that is currently the greatest limitation…” [10].

Evaluation of MODIS NPP and GPP products across multiple biomes

Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25 km 2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites — often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites — often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters.

A global framework for monitoring phenological responses to climate change

Remote sensing of vegetation phenology is an important method with which to monitor terrestrial responses to climate change, but most approaches include signals from multiple forcings, such as mixed phenological signals from multiple biomes, urbanization, political changes, shifts in agricultural practices, and disturbances. Consequently, it is difficult to extract a clear signal from the usually assumed forcing: climate change. Here, using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight‐element monthly climatology, we identified pixels whose wavelet power spectrum was consistently dominated by annual cycles and then created phenologically and climatically self‐similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity. Remaining phenoregions represented areas with a minimized probability of non‐climatic forcings and form elemental units for long‐term phenological monitoring.

Wildlife-habitat relationships in Oregon and Washington

Wildlife-habitat relationships have been described conceptually (Morrison and others 1998) and for individual species at regional scales (Maser and others 1984; Thomas 1979; Brown 1985). Wildlife-Habitat Relationships of Oregon and Washington updates these works and provides a timely synthesis and summary of existing knowledge of wildlife-habitat relationships in these 2 states. Multi-scale relationships and theory are addressed for 743 terrestrial vertebrates both in a traditional text synthesis format and in CD-ROM data matrices compiled from existing literature and expert panel assessments. This massive (736 pages and 27 chapters) book and its accompanying data reflect the collaborative efforts of over 600 participants, including 88 authors and 34 sponsors. We read this body of work cover-to-cover and put the CD-ROM to the test as part of a graduate course at Oregon State University. Our consensus of this contribution stems from numerous lively discussions, some with wildlife-habitat professionals, particularly Tom O'Neil, Mark Meyers, and Bruce Marcot. Our findings are applausive. This work provides a useful standard for description and discussion of Pacific Northwest habitats and elements, develops unique conceptual themes relevant to species' relationships both regionally and globally, and provides an innovative and interactive tool for information retrieval via the CD-ROM insert. Although it has some shortcomings, the book provides a lucid treatment of a broad array of wildlife subjects across multiple biomes, including temperate coniferous rainforest, shrubgrassland, and coastal-marine areas. This book serves as a foundation reference for wildlife biologists and natural resource managers attempting to understand particular habitats, wildlife species, or management contexts. This is a complex and comprehensive book, and there are too many specifics and authors to cite. Instead, we provide a summary and identify key innovations and limitations of the text. The authors derive 32 wildlife for Oregon and Washington. Cluster analysis is used to collapse species-specific cover type associations into broad habitat types based on similarity of associated wildlife species groups (Chapter 1). This is a satisfying approach in that multi-species data on distribution patterns are used to determine the broad scale types, as opposed to subjectively defining categories. The resulting habitats are characterized (Chapter 2, >30 p color photographs; Chapter 5, Tables 1 to 3) and mapped across Oregon and Washington (pp vii-xi). In addition, heterogeneity is acknowledged by a summary of natural disturbance regimes, succession dynamics, and management impacts. We anticipate that Chapter 2 will be frequently cited to provide basic descriptions. We identified 2 issues of concern in the formulation and presentation of the broad types. First, we noted that the initial vegetative cover associations used in the cluster analysis