B41A-0084 0800h
Climate Related Vegetation Characteristics Derived From MODIS LAI and NDVI
MODIS-based Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) are used to examine detailed information regarding growing season and total annual production across the globe. Overall, MODIS LAI has larger variability and demonstrates more information regarding the evolution and structure of the seasonal vegetation characteristics. In contrast, the NDVI saturates around 0.7 and tends to overestimate the length of the growing season in regions where it is already long. Next, a Climatic Impact Index (CII) is derived to provide additional information regarding the potential sensitivity of vegetation to changes in climatic variables by accounting for the length of growing season. By normalizing the growth rate to the biome-average growth rate, this index can identify fractional loss of annual production during a given month, as opposed to the absolute loss which may be strongly weighted by the overall growth rate for different ecosystems. Our index provides a quantitative framework for assessing the importance of the length of the growing-season in determining climatic vulnerability and highlights regions such as the Sahel, eastern Africa, and central southwest Asia, which are highly susceptible to climate-induced variability during their short but intense growing seasons. In the last part of the paper, we use the long time series AVHRR products as a substitute for the MODIS products, and test the temporal characteristics of the CII, which is termed the CVII (Climate-Variability Impact Index). Major drought events are well-captured by the CVII, suggesting potential use as a monitoring and evaluation tool. Furthermore, the strong positive correlation between the CVII and the Vegetation Condition Index (VCI) suggests that the CVII can quantitatively identify the effects of climatic variability upon vegetation activity. Finally, CVII is used to generate models to monitor and predict the crop production at different temporal and spatial scales. Overall, these results demonstrate that the LAI-based CVII can be applied as a possible monitoring tool in agriculture applications.
B41A-0085 0800h
Seasonal Changes in Remote Vegetation Indices and Net Photosynthesis of Japanese Larch Needles
We investigated the seasonal pattern of four kinds of remote vegetation indices (NDVI, PRI, [(1/rRedEdge)-(1/rNIR)] and [(1/rGreen)-(1/rNIR)]) and their correlation to photosynthetic activity in the needle leaves of Japanese larch. In the 42-year-old larch forest (Tomakomai, Japan), the diurnal courses of spectral reflectance and gas exchange rates of larch needles were periodically investigated during early June to late October in 2003. In the Tomakomai larch forest, expansion of short-shoot needles was started from mid-May, and yellow color change of the needle leaves was observed in late October. The seasonal pattern of index value differed among the vegetation indices. For example, daytime mean NDVI showed constant value from late June to early October. The [(1/rRedEdge)-(1/rNIR)] and PRI were increased during summer, and their peak were observed in July and August, respectively. Although the values of NDVI, PRI and [(1/rRedEdge)-(1/rNIR)] were depressed in late October with autumn senescence of the needles, the [(1/rGreen)-(1/rNIR)] in larch needles was not changed even in yellow colored needles. Consequently, correlation of these vegetation indices and seasonal changed photosynthetic parameters such as net photosynthetic rate (Pn) and photosynthetic light use efficiency (LUE) also differed among the indices. Although the PRI, NDVI and [(1/rRedEdge)-(1/rNIR)] positively correlated with daily maximum Pn and daily means of Pn and LUE, no correlation was found between [(1/rGreen)-(1/rNIR)] and the measured photosynthetic parameters. Based on the results of Pearson_fs correlation test, PRI and [(1/rRedEdge)-(1/rNIR)] were considered to be most useful index for the estimations of seasonal changes in Pn and LUE, respectively.
B41A-0086 0800h
The North Atlantic Oscillation and Regional Phenology Prediction Over Europe.
We present an integrated modeling study designed to investigate changes in ecosystem level phenology over Europe associated with changes in a climate pattern, the North Atlantic Oscillation (NAO). We derived onset dates from processed NDVI data sets and used growing degree day summations from the NCEP reanalysis to calibrate and validate a phenology model to predict the onset of the growing season over Europe. In a cross validation hindcast, the model (PHENOM) is able to explain 63% of the variance in onset date for grid cells containing at least 50% mixed and boreal forest. Using a model developed from previous work we performed climate change scenarios, generating synthetic temperature and growing degree day distributions under a hypothetically increasing NAO. These new distributions were used to drive PHENOM and project changes in the timing of onset for forested cells over Europe. Results from the climate change scenarios indicate that, if the current trend in the NAO continues, there is the potential for a continued advance to the start of the growing season by as much as 13 days in some areas.
B41A-0087 0800h
Land Surface Phenology and Climatic Variation in the IGBP High-Latitude Transects.
The International Geosphere-Biosphere Program (IGBP) has delineated five study areas that form a northern high-latitude network for the analyses of vegetation dynamics, carbon dynamics, and water and energy exchanges. The five transects are located in Far East Siberia (FEST), East Siberia (EST), Scandinavia and northern Europe (ScanTran), boreal forest in Canada (BFTCS), and Alaska (AT). We examined the magnitude and significance of changes in land surface phenology in these transects using the Pathfinder AVHRR Land (PAL) dataset, which consists of maximum NDVI 10-day composites at 8 km spatial resolution. Daily minimum and maximum temperatures were extracted from the NCEP/NCAR Reanalysis Project to generate daily growing degree-days (base 0 oC) as 10-day composites (GDD10) and as seasonal accumulations (AGDD). Each transect spans a range of climatic and land surface conditions; thus, we partitioned the area within each transect according to the World Wildlife Fund's global ecoregions. For each of 24 ecoregions distributed over the five transects, we tested for significant differences in average GDD10 and average NDVI from April and September between the years corresponding to the observational periods of NOAA-9 (1985-1988) and NOAA-14 (1995-1999). We used the seasonal Mann-Kendall trend test to detect significant trends within these time periods for GDD10 and NDVI. Finally, we modeled the land surface phenology using a nonlinear spherical model to relate the PAL NDVI data from the first half of the growing season to AGDD. Models for each ecoregion within each transect were fit for each of the two satellite periods separately and then their parameter estimates were compared. We found significant increases and trends in average GDD10 in Canadian and Alaskan transects only. The other three transects exhibited neither significant step changes nor trends in GDD10 in any ecoregion. Furthermore, we found significantly higher NDVI in 3 of 5 ecoregions in ScanTran and in 2 of 6 ecoregions in AT. Spherical models fit well the first half of the growing season for 22 of 24 ecoregions with pseudo-R2's ranging from 0.65 to 0.95. The majority of ecoregions displayed equivalent NDVI for low AGDD in both study periods. The FEST is the only transect that displayed a nearly consistently higher sill in the spherical model from 1995 to 1999, indicating a higher NDVI plateau for larger AGDD. In 10 of 24 ecoregions distributed across the transects, the start of the growing season advanced slightly but significantly (between 2 and 18 days) between the first and second study periods; however, this progression seems to be more pronounced and larger in Alaska and Canada than in the other transects.
B41A-0088 0800h
Changes In Growing Season Determined From Alaska GLOBE Data And NDVI
The GLOBE program is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. Through its suite of data collection and extensive network, GLOBE provides a valuable resource for studying the impact of climate change on the environment. Students make observations and measurements on the soils, hydrology, land cover, climate and phenology at or near their schools and report their data through the Internet to the GLOBE data archive. Since 1999, GLOBE students have monitored plant phenology at their schools and have reported annual dates for bud burst, green-up, leaf growth, and green-down for selected trees, shrubs, and grasses. GLOBE students have made over 54,000 phenology measurements with nearly half that data collected by GLOBE students in Alaska. This study reviews the Alaska phenology measurements to discern long-term changes from interannual variations in plant growing season length. Satellite derived vegetation indices from MODIS and AVHRR are also used in this study.
B41A-0089 0800h
Plant functional type modulates ecosystem response to warming: A case study from boreal forest ecosystems in interior Alaska
Over the last several decades, surface air temperatures have increased substantially in Arctic tundra and boreal forest biomes in the Northern Hemisphere, particularly during spring. The net effect of this warming on the net carbon balance of northern ecosystems remains uncertain because it simultaneously accelerates soil thaw, the onset of photosynthesis, and rates of soil decomposition, in addition to affecting other ecosystem processes. In April of 2002, we established eddy covariance systems to measure ecosystem-atmosphere CO2 exchange in a fire chronosequence with burns occurring in 1999, 1987 and ~1920. The sites are in a well-drained (upland) area in interior Alaska where perennial grasses are abundant at the 1999 burn site, aspen and willow are dominant at the 1987 burn site, and black spruce is dominant at the ~1920 burn site. Here we compared April to September CO$_{2}$ fluxes from 2002 with those from 2003. The spring of 2003 was warmer and drier than in 2002 with mean April air temperatures of -4°C in 2002 and 0.5°C in 2003. The warmer spring in 2003 substantially enhanced net ecosystem uptake in the 1920 burn in May and June. In contrast, enhanced net ecosystem uptake occurred in the 1987 burn only during June, suggesting less plasticity in the response of deciduous plant functional types to variability in spring climate than in evergreen conifers. The net ecosystem exchange (NEE) fluxes integrated from April to September for 2002 and 2003 were 0.0 and -0.3 g C m-2 day-1 for the 1999 burn site, -0.8 and -1.2 g C m-2 day-1 for the 1987 burn site, and -1.1 and -1.2 g C m-2 day-1 for the 1920 burn site.
B41A-0090 0800h
Importance Of Recent Shifts In Soil Thermal Dynamics On Growing Season Length, Productivity, And Carbon Sequestration In Terrestrial High-Latitude Ecosystems
In terrestrial high-latitude regions, observations indicate recent phenological changes in snow cover extent and soil freeze-thaw transitions due to climate change. These modifications may result in temporal shifts in the growing season and the associated rates of terrestrial productivity. Changes in productivity will influence the ability of these ecosystems to sequester atmospheric CO$_{2}$. We use the Terrestrial Ecosystem Model (TEM), which simulates the soil thermal regime, in addition to terrestrial carbon, nitrogen and water dynamics, to explore these issues by examining trends over recent decades in extratropical regions ($30\deg$-$90\deg$ N). The TEM simulations indicate snow cover duration has decreased by approximately 6-8 days between the years 1972-2000. This result is generally consistent with NOAA satellite observations, which show a decrease between 3-6 days per decade since 1972. This modeled decrease in snow cover is associated with a trend towards an earlier thaw date of frozen soils and the onset of the growing season in the spring by approximately 1-2 days between 1960 and 1980 and 3-5 days from 1980-2000. Between 1988 and 2000, satellite records show a slightly stronger trend in thaw and the onset of the growing season, averaging between 5-8 days earlier. Although the 1960s-1980s showed a less pronounced trend in earlier thaw dates and increased growing season length, our model analyses indicate increasing net carbon uptake at the decadal scale. The regions between $60\deg$ - $90\deg$ N have acted as a source of 0.05 Pg C in the 1960s, 0.03 Pg C in the1970s, 0.01 Pg C in the 1980s, and ultimately shifting to a sink of 0.04 Pg C during the 1990s. Taking into account the effects of changes in land-use in the region between $30\deg$ - $60\deg$ N, the TEM results show a gradual enhancement in net carbon uptake from 0.56 Pg C in the 1960s to 1.0 Pg C in the 1990s. Our results reveal noteworthy patterns of phenological change in growing season and productivity over the past four decades, indicating that prediction of terrestrial carbon dynamics from one decade to the next will require that large-scale models adequately take into account the corresponding changes in soil thermal regimes.
B41A-0091 0800h
CO$_{2}$, Temperature, and Soil Moisture Interactions Affect NDVI and Reproductive Phenology in Old-Field Plant Communities
Plant community composition and ecosystem function may be altered by global atmospheric and climate change, including increased atmospheric [CO$_{2}$], temperature, and varying precipitation regimes. We are conducting an experiment at Oak Ridge National Laboratory (ORNL) utilizing open-top chambers to administer experimental treatments of elevated CO$_{2}$ (+300 ppm), warming (+ 3 degrees Celsius), and varying soil moisture availability to experimental plant communities constructed of seven common old-field species, including C$_{3}$ and C$_{4}$ grasses, forbs, and legumes. During 2004 we monitored plant community phenology (NDVI) and plant reproductive phenology. Early in the year, NDVI was greater in wet treatment plots, and was unaffected by main effects of temperature or CO$_{2}$. This result suggests that early in the season warming is insufficient to affect early canopy development. Differences in soil moisture sustained throughout the winter and into early spring may constitute an important control on early canopy greenup. Elevated CO$_{2}$ alleviated detrimental effects of warming on NDVI, but only early in the season. As ambient temperatures increased, elevated temperatures negatively impacted NDVI only in the dry plots. Wetter conditions ameliorate the effects of warming on canopy greenness during the warmer seasons of the year. Warming increased rates of bolting, number of inflorescences, and time to reproductive maturity for {\it Andropogon virginicus} (a C$_{4}$ bunchgrass). {\it Solidago Canadensis} (a C$_{3}$ late-season forb) also produced flowers earlier in elevated temperatures. Conversely, none of the C$_{3}$ grasses and forbs that bolt or flower in late spring or early summer responded to temperature or CO$_{2}$. Results indicate that warming and drought may impact plant community phenology, and plant species reproductive phenology. Clearly community phenology is driven by complex interactions among temperature, water, and CO$_{2}$ that change throughout the season. Our data stresses the importance of multifactor experiments examining global change, given that often plants respond to interactions of multiple factors rather than solely main effects.
B41A-0092 0800h
Birds, Plants, and Climate: Impacts of Climatic Change on the Phenology of Spring Bird Migration in the Great Lakes, USA
Global climate change is likely to emerge as a significant, if not dominant force, in ecosystem change over the next several decades. While the potential impacts of discordant range shifts have received considerable attention, asynchronies in phenology have received less attention. Migrating birds are of particular concern given their need of multiple habitats, which often involve large spatial scales. Time is of the essence for migrating birds: it is critical for departures with favorable weather conditions, intersecting adequate resources to fuel further flight, and for spring migrants, arrival on the breeding grounds in concert with the flush of food to feed offspring. We assess changes in the spring phenology of migrant birds in the Great Lakes region using observations in Germfask, MI, USA ($49\deg$17'N, $85\deg$57'W) from 1965-1994 and Fairfield Township, WI,USA ($43\deg$30'N, $89\deg$30'W) from 1976-1999. We correlate the species temporal changes with abiotic and biotic variables to understand how migrants' behaviour is associated with spring green-up (plant phenology) and multi-scalar climate/weather variables, such as the North Atlantic Oscillation, and local and regional temperature. We assess the observed changes and correlations in light of migrant life history factors (wintering grounds, diet, residency) to understand which species, or groups of species, are particularly at risk from climatic-change-induced disruptions to the migratory and breeding schedule. The implications of potential ecosystem asynchronies between climate/weather, migratory schedules, and the arrival of spring are discussed.
B41A-0093 0800h
A Robust, Bayesian Approach to the Analysis of Vegetative Phenology Using Satellite Vegetative Indices in the Presence of Outliers, Noisy Data and Data Gaps
Vegetated cover is affected over time by such factors as long and short term climate changes, inter-annual climate variability, changes in the hydrologic cycle, and anthropogenic land cover change. Although remote sensing has revolutionized the way vegetation changes are observed, the full potential is often hampered by missing data due to instrumentation, weather conditions (clouds) or ground cover (snow). These breaks in the time series make it difficult, if not impossible, to employ a number of classical time series methods - such as standard Fourier analysis - to characterize the phenology of vegetation for individual years and between years. We have constructed a recursive least-square (LS) algorithm, drawing on concepts from Bayesian statistics, that generates a robust polynomial interpolation of a noisy, biased, discontinuous data set. The heart of our procedure employs an asymmetric weighted LS technique to jointly (simultaneously) fit time series data on a pixel-by-pixel basis to two classes of polynomials. One polynomial accounts for long-term change and inter-annual fluctuations. The second class of polynomials consists of a set of annual 8-th order spline functions constrained to observe certain continuity constraints between years. Estimated data points that are substituted for missing data are down-weighted by an adjustable measure to influence the high-order intra-annual curve-fit. Thus the curve-fit model relies mainly on observed data, when they are available, but is stabilized by the estimated data. Preliminary testing of our algorithm demonstrated its stability was vulnerable when dealing with data sets having substantial gaps over extended periods of time. Our solution was to pre-condition these data gaps by introducing estimated data based on an a priori estimate of the average annual cycle plus long term, low-order variations for each pixel. In addition, a minimum roughness criterion was invoked for the average annual cycle during times when the expectation of data gaps was highest. Currently, we represent the mean annual cycle by the first four annual harmonics (T = 12, 6, 4 and 3 months). To evaluate the algorithm, we applied it to satellite vegetation data from a semi-arid ecosystem in the Great Basin. The data consisted of 1 km$^{2}$ pixels of weekly Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) between 1990-2001. Long-term sensor drift - typical of NDVI data - is accounted for by removing a jointly estimated average trend plus annual fluctuation based on non-vegetated regional salt flat data. Therefore, the resultant data set consists of "differential NDVI data" (DNDVI) - the difference between observed NDVI values and those average NDVI values from regional non-vegetated areas. Conditioning the DNDVI "signal" with this algorithm makes it possible to clearly visualize patterns in the time series of each pixel, as well as to continuously simulate temporal changes over a gridded area. One can accordingly identify the overall vegetation phenology, specifically the timing of green-up, peak greenness, senescence, and duration of greenness, among other characteristics; thus setting the stage for identifying and discriminating among, principle vegetative classes.
B41A-0094 0800h
Distinguishing Inter-annual Phenological Variability from Long-term Change in the Great Basin Using a new Method of Time-Series Modeling
Semi-arid vegetation communities are affected over time by a host of factors, including climate change, inter-annual climate variability, changes in hydrologic cycle, and land use-land cover change. Because vegetation exhibits inter-annual variability, it is important to determine characteristic inter-annual responses of semi-arid vegetation communities to distinguish variability from change. We explore the phenologies of five major ecosystem types located in central and northern Nevada: dry desert shrub, sagebrush steppe, annual grassland, pinyon-juniper woodland, and montane perennial grassland. By identifying the phenological characteristics of these major Great Basin land cover classes, we constrain the range of expected inter-annual and decadal variability. Time series of known vegetation types are analyzed by applying a new methodology to characterize inter-annual vegetation phenology using time series of 1 km Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) from 1990-2001. We characterize vegetation community response on both inter-annual and decadal time scales using a curve-fit algorithm that is flexible enough to address change over time without being influenced by error caused by sensor drift, clouds, snow, or missing data. Our methodology uses least mean squares to fit weekly and biweekly NDVI data simultaneously to a low-order baseline polynomial, which models both long-term change and mean inter-annual periodicity, and a high order annual polynomial, which allows for flexible phenologies between years. Long-term sensor drift is accounted for by removing a fit to mean NDVI from non-vegetated surfaces salt flats in Nevada and Utah prior to analysis. Anomalously low data caused by clouds or snow are removed by identifying the standard deviation of the curve-fit during the stable fall months and removing all points within one standard deviation of zero (all negative values are also excluded). Missing points removed due to clouds and snow or absent in 1994 are replaced with estimated values from the low-order baseline polynomial value at that point. Thus the model relies mainly on real data, but is stabilized by estimated data, particularly during winter and early spring when clouds and snow are common. The resulting robust, continuous model of NDVI can be used to detect subtle shifts in inter-annual vegetation response that might otherwise be masked by uncertainty in the NDVI time series. Initial analyses highlight three distinct types of change in the Great Basin linked to pinyon-juniper woodland, cheatgrass dominated grasslands and riparian marshlands. Dry desert shrub, sagebrush steppe, montane perennial grassland and pinyon-juniper do not show high inter-annual variability except in cases where green-up is offset by early spring snow. As a result, long-term change is more likely to be detectable. For example, some pinyon-juniper woodlands may show increasing greenness linked to woody expansion into neighboring ecosystems. Annual grasslands dominated by cheatgrass show a high degree of inter-annual variability in response to rainfall, making long-term change in this community difficult to identify. An example of change detected in a minor ecosystem type is observed in marshlands east of Nevada's Ruby Mountains. Here, an increasing greenness trend above the normal range of inter-annual variability may be linked to changes in water management and land use. With the combination of field localities of known land cover and a robust curve-fit NDVI time series, phenological variability can be distinguished from long-term change.
B41A-0095 0800h
Exploring Climate Driven Dynamics in Vegetation Phenology Using Data from MODIS
Vegetation phenology is an effective indicator of intra-annual dynamics in vegetation growth caused by climate variability. The aim of this study is to use global estimates of vegetation phenological transition dates to (1) examine the controls of climate forcing on global phenological patterns; and (2) to assess the linkage between satellite observations and field measurements. To achieve these goals, we used time series data from NASA�s Moderate Resolution Imaging Spectroradiometer (MODIS). To estimate phenological transition dates from MODIS data, piecewise sigmoidal models were fit to annual trajectories of the enhanced vegetation index computed from MODIS nadir bidirectional reflectance distribution function adjusted reflectances for each pixel at 1 km resolution, globally. Using these models, it is relatively straightforward to identify phenological transition dates over vegetated land areas, including areas with multiple growth cycles. The resultant phenological patterns were then related to MODIS land surface temperature data and precipitation data from the Tropical Rainfall Measuring Mission. The results from this analysis reveal strong relationships between phenology and land surface temperature in the temperate mid-latitudes, and strong covariance between phenology and precipitation in semi-arid regions, especially in regards to the timing of greenup onset. Finally, validation efforts using field measurements show good agreement between satellite-derived transition dates and in-situ measurements. These results provide strong support regarding the quality of global phenological retrievals from MODIS.
B41A-0096 0800h
Vegetation Phenology and MODIS Leaf Area Index in the Albemarle-Pamlico Basin, North Carolina and Virginia, U.S.A.
The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on Terra and Aqua satellite platforms offer unprecedented opportunities for daily to monthly monitoring of terrestrial vegetation dynamics. MODIS product MOD15A2 Leaf Area Index (LAI) maps the projected leaf surface area every 8 days at a pixel resolution of 1 km. LAI is an input to biogenic emission models that estimate volatile organic compounds emitted by vegetation. LAI is also used in modeling the leaf surface area available for deposition of atmospheric particles and gases. As an integrator of vegetation phenology and landscape characteristics, LAI has utility in land cover classification and ecoregion delineation. This report summarizes aspects of validation efforts comparing field LAI measurements with MODIS LAI image time series. Eight forested piedmont and coastal plain field sites in the Albemarle-Pamlico Basin were visited intermittently in the 2001-2004 growing seasons. In addition to baseline forest biometric characterization at each site, field measurements of LAI were made using two optical methods: hemispherical photography and TRAC (Tracing Radiation and Architecture of Canopies) sunfleck profiling. LAI surface maps were created from regression of field LAI measurements with Normalized Difference Vegetation Index (NDVI) maps derived from precision registered Landsat ETM+ imagery. The LAI surface maps (30 m pixels) were then coarsened to the 1 km scale for comparison with the MODIS LAI product to characterize the uncertainty in LAI estimation.
B41A-0097 0800h
Abundance of Woody Riparian Species in the Western USA in Relation to Phenology, Climate, and Flow Regime
We randomly selected 475 long-term U.S. Geological Survey stream gaging stations in 17 western states to relate the presence and abundance of woody species to environmental factors. Along a 1.3-km reach near each station we measured the cover of all species on a list of the 44 most abundant large woody riparian species in the region. We used logistic regression to fit the response of four abundant species to growing degree days and mean precipitation. Then we related relative abundance of these 4 species to timing of the flood peak in sites where the likelihood of occurrence was greater than 0.5. The exotics {\it Tamarix ramosissima} (saltcedar) and {\it Elaeagnus angustifolia} (Russian-olive) are now the third and fourth most frequently occurring large woody riparian species in the western U.S. and the second and fifth most abundant. In climatically suitable areas, species differences in reproductive phenology produce different relations of abundance to flow regime. Because of its limited period of seed release and viability in early summer, cottonwood ({\it Populus deltoides}) is disadvantaged where floods occur in the spring or fall. Abundances of saltcedar, because of its long period of seed release; Russian-olive, because of seed dormancy; and {\it Salix exigua}, because of the importance of vegetative spread, are much less sensitive to flood timing.
B41A-0098 0800h
Changes in the onset of spring in the western United States
By two separate measures, spring bloom timing and the timing of snowmelt runoff, spring onset has advanced by approximately one-three weeks over a broad region of the western United States since the late 1970's. This change is related to warmer springs over the region, which in turn may be related to large scale climate change.
B41A-0099 0800h
Phytoplankton Community Change in the Eocene Greenhouse
Dinoflagellate cyst data are presented from core 16/28-sb01 from the northern flank of the Porcupine High, 220 km west of county Mayo, Ireland (54N, 13W). The Porcupine High is currently on the eastern fringe of the North Atlantic Drift (NAD). The core represents a window onto part of the Early Eocene Climatic Optimum (EECO, 52-50 Ma) because a thick (60 m) unit of homogenous calcareous muds is present containing calcareous nannoplankton from entirely within NP12 (c. 53-51 Ma). Dinoflagellate cysts are diverse (118 taxa; 41 samples) and record environmental changes over time. Detrended correspondence analyses indicate that there are no pronounced changes in composition between contiguous samples but long-term changes in composition occur instead. Separate ordinations on presence-absence and relative abundance data demonstrate that changes in the co-occurrence patterns of taxa (presence-absence matrix) are separate from changes in relative abundance. To further show composition changes, we applied the squared chord distance metric to the relative abundance matrix and plotted the distances between every sample. On average, samples become more dissimilar to one another with increasing distance apart. The Bray-Curtis metric was applied to the presence-absence matrix and also demonstrates that on average samples are less similar to one another with increasing distance apart. These results are statistically significant. They are probably not related to sampling intensity either; bootstrap, jackknife 2, rarefaction and Chao 1 all show that after c. 15 samples have been randomly pooled, new taxa are encountered with very low frequency. Changes in the P:G ratio and the decreasing abundance of sporomorphs indicates that basin deepening drove community change. Provisionally, our results indicate that; (1) if the NAD was active at this time, it was stable in the EECO, (2) community change is directional and responds to regional processes, (3) changes in relative abundance are not necessarily coupled with changes in co-occurrence patterns.