Interannual variability of NDVI and its relationship to climate for North American shrublands and grasslands

Abstract. Our objective was to analyse the interannual variability of different characteristics of the seasonal dynamics of NDVI and their relationships with climatic variables for grassland and shrubland sites of North America. We selected twenty-five sites located in relatively undisturbed areas. We analysed the variability of seven traits derived from the annual dynamics of the NDVI at each site: the annual integral, the difference between maximum and minimum NDVI, the dates of the inflection points of a double logistic model fitted to the NDVI curve, the difference between these dates, the date of maximum NDVI, and the coefficient of determination of the double logistic model. The temporal variability of traits that integrated aspects of primary productivity over the year was lower than those related to seasonality. This suggests that from year to year, grassland and shrubland ecosystems would differ more in the timing of production and senescence than in the total amount of carbon fixed. The integral of NDVI showed less temporal variability than annual precipitation. The coefficient of variation of both precipitation and the NDVI integral were positively related. The slope of the relationship was significantly lower than 1, indicating that the variability of ecosystem function is a lower proportion of the variability of annual precipitation in areas with a high relative variability of this climatic variable than in areas of low variability. The variability of most of the NDVI traits analysed showed a negative and, in general, non-linear relationship with annual precipitation. The same kind of relationship has been reported elsewhere for annual precipitation and its coefficient of variation. Mean annual precipitation has been reported as the main control of above-ground net primary production in grassland and shrubland ecosystems. Our results suggest that this climatic variable is also associated with the interannual variability of carbon gains, such as the primary production and its seasonality.     

Regional patterns of ecosystem functional diversity in the Argentina Pampas using MODIS time-series

The characterization of ecosystem functioning is significant for different purposes such as biodiversity conservation and ecosystem services. A key aspect of ecosystem functioning is carbon gains, since it represents the energy available for upper trophic levels. In this sense, remote-sensing methods have allowed the study of ecosystem dynamics and spatial distribution at different spatial and temporal scales. The objectives were to describe the regional patterns of ecosystem functional diversity and to establish the importance of interannual variability in the definition of Ecosystem Functional Types (EFTs) in the Argentina Pampas. EFTs were obtained from carbon gains using a set of seven functional attributes and their interannual variations, which were retrieved from 14-year NDVI time-series. An ISODATA technique was applied to all the analyzed variables, and the clusters that best separate in the n-dimensional space were selected using discriminant analysis. The Argentina Pampas shows a high heterogeneity in the spatial patterns of ecosystem functional attributes. The annual integral of NDVI (i-NDVI, a linear estimator of net primary productivity), a complex of ecosystem functional attributes that describe the interannual variability, and the annual relative range of NDVI (RREL, ecosystem seasonality) had the highest relevance to distinguish nine EFTs in the study area. This study shows a novel approach for mapping ecosystem functioning, which reveals the importance of interannual variations. This methodology includes the effects of climate variability on ecosystem dynamics, thus enhancing our understanding of ecosystem functional diversity. The results obtained represent a baseline scenario to evaluate the effects of both land use change and climate variability on ecosystem functioning from a temporal perspective.     

Weather, not climate, defines distributions of vagile bird species

Background

Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.

Methodology

We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS) using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed “weather” models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality) from the three month, six month and one year period preceding each bird record over a 58 year period (1950–2008). These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.

Conclusions

Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species'' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.     

Desertification alters regional ecosystem–climate interactions

We studied the spatial patterns and temporal dynamics of vegetation structural responses to precipitation variation in grassland, transitional, and desertified‐shrubland ecosystems in an 800 km² region of Northern Chihuahua, USA. Airborne high‐fidelity imaging spectroscopy data collected from 1997 to 2001 provided spatially detailed measurements of photosynthetic and senescent canopy cover and bare soil extent. The observations were made following wintertime and summer monsoonal rains, which varied in magnitude by >300% over the study period, allowing an assessment of ecosystem responses to climate variation in the context of desertification. Desertification caused a persistent increase in both photosynthetic vegetation (PV) and bare soil cover, and a lasting decrease in nonphotosynthetic vegetation (NPV). We did not observe a change in the spatial variability of PV cover, but its temporal variation decreased substantially. In contrast, desertification caused the spatial variability of NPV to increase markedly, while its temporal variation did not change. Both the spatial and temporal variation of exposed bare surfaces decreased with desertification. Desertification appeared to be linked to a shift in seasonal precipitation use by vegetation from mainly summer to winter inputs, resulting in an apparent decoupling of vegetation responses to inter‐annual monsoonal variation. Higher winter rainfall led to decreased springtime spatial variability in the PV cover of desertified areas. Higher summer rainfall resulted in decreased PV cover variation in grassland, transition and desertified‐shrubland regions. The effects of desertification on NPV dynamics were more than three times greater than on PV or bare soil dynamics. Using remotely sensed PV and NPV as proxies for net primary production (NPP) and litter dynamics, respectively, we estimated that desertification decreases the temporal variability of NPP and increases spatial variation of litter production and loss. Quantitative studies of surface biological materials and ecosystem processes can now be measured with high ‘structural’ detail using imaging spectroscopy and shortwave‐infrared spectral mixture analysis.     

Testing Bergmann's rule and the resource seasonality hypothesis in Malagasy primates using GIS-based climate data

We tested four major hypotheses on the ecological aspects of body mass variation in extant Malagasy strepsirrhines: thermoregulation, resource seasonality/scarcity, resource quality, and primary productivity. These biogeographic hypotheses focus on the ecological aspects of body mass variation, largely ignoring the role of phylogeny for explaining body mass variation within lineages. We tested the independent effects of climate and resource-related variables on variation in body mass among Malagasy primates using recently developed comparative methods that account for phylogenetic history and spatial autocorrelation. We extracted data on lemur body mass and climate variables for a total of 43 species from 39 sites. Climatic data were obtained from the WorldClim database, which is based on climate data from weather stations compiled around the world. Using generalized linear models that incorporate parameters to account for phylogenetic and spatial autocorrelation, we found that diet and climate variables were weak predictors of lemur body mass. Moreover, there was a strong phylogenetic effect relative to the effects of space on lemur body mass in all models. Thus, we failed to find support for any of the four hypotheses on patterns of geography and body mass in extant strepsirrhines. Our results indicate that body mass has been conserved since early in the evolutionary history of each genus, while species diversified into different environmental niches. Our findings are in contrast to some previous studies that have suggested resource and climate related effects on body mass, though these studies have examined this question at different taxonomic and/or geographic scales.     

Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems

Aim The controls of gross radiation use efficiency (RUE), the ratio between gross primary productivity (GPP) and the radiation intercepted by terrestrial vegetation, and its spatial and temporal variation are not yet fully understood. Our objectives were to analyse and synthesize the spatial variability of GPP and the spatial and temporal variability of RUE and its climatic controls for a wide range of vegetation types. Location A global range of sites from tundra to rain forest. Methods We analysed a global dataset on photosynthetic uptake and climatic variables from 35 eddy covariance (EC) flux sites spanning between 100 and 2200 mm mean annual rainfall and between ?13 and 26°C mean annual temperature. RUE was calculated from the data provided by EC flux sites and remote sensing (MODIS). Results Rainfall and actual evapotranspiration (AET) positively influenced the spatial variation of annual GPP, whereas temperature only influenced the GPP of forests. Annual and maximum RUE were also positively controlled primarily by annual rainfall. The main control parameters of the growth season variation of gross RUE varied for each ecosystem type. Overall, the ratio between actual and potential evapotranspiration and a surrogate for the energy balance explained a greater proportion of the seasonal variation of RUE than the vapour pressure deficit (VPD), AET and precipitation. Temperature was important for determining the intra‐annual variability of the RUE at the coldest energy‐limited sites. Main conclusions Our analysis supports the idea that the annual functioning of vegetation that is adapted to its local environment is more constrained by water availability than by temperature. The spatial variability of annual and maximum RUE can be largely explained by annual precipitation, more than by vegetation type. The intra‐annual variation of RUE was mainly linked to the energy balance and water availability along the climatic gradient. Furthermore, we showed that intra‐annual variation of gross RUE is only weakly influenced by VPD and temperature, contrary to what is frequently assumed. Our results provide a better understanding of the spatial and temporal controls of the RUE and thus could lead to a better estimation of ecosystem carbon fixation and better modelling.     

Influence of long-distance climate teleconnection on seasonality of water temperature in the world's largest lake--Lake Baikal, Siberia

Large-scale climate change is superimposed on interacting patterns of climate variability that fluctuate on numerous temporal and spatial scales--elements of which, such as seasonal timing, may have important impacts on local and regional ecosystem forcing. Lake Baikal in Siberia is not only the world's largest and most biologically diverse lake, but it has exceptionally strong seasonal structure in ecosystem dynamics that may be dramatically affected by fluctuations in seasonal timing. We applied time-frequency analysis to a near-continuous, 58-year record of water temperature from Lake Baikal to examine how seasonality in the lake has fluctuated over the past half century and to infer underlying mechanisms. On decadal scales, the timing of seasonal onset strongly corresponds with deviation in the zonal wind intensity as described by length of day (LOD); on shorter scales, these temperature patterns shift in concert with the El Nino-Southern Oscillation (ENSO). Importantly, the connection between ENSO and Lake Baikal is gated by the cool and warm periods of the Pacific Decadal Oscillation (PDO). Large-scale climatic phenomena affecting Siberia are apparent in Lake Baikal surface water temperature data, dynamics resulting from jet stream and storm track variability in central Asia and across the Northern Hemisphere.     

Climate and the inter‐annual variability of fire in southern Africa: a meta‐analysis using long‐term field data and satellite‐derived burnt area data

Aim This study investigates inter‐annual variability in burnt area in southern Africa and the extent to which climate is responsible for this variation. We compare data from long‐term field sites across the region with remotely sensed burnt area data to test whether it is possible to develop a general model. Location Africa south of the equator. Methods Linear mixed effects models were used to determine the effect of rainfall, seasonality and fire weather in driving variation in fire extent between years, and to test whether the effect of these variables changes across the subcontinent and in areas more and less impacted by human activities. Results A simple model including rainfall and seasonality explained 40% of the variance in burnt area between years across 10 different protected areas on the subcontinent, but this model, when applied regionally, indicated that climate had less impact on year‐to‐year variation in burnt area than would be expected. It was possible to demonstrate that the relative importance of rainfall and seasonality changed as one moved from dry to wetter systems, but most noticeable was the reduction in climatically driven variability of fire outside protected areas. Inter‐annual variability is associated with the occurrence of large fires, and large fires are only found in areas with low human impact. Main conclusions This research gives the first data‐driven analysis of fire–climate interactions in southern Africa. The regional analysis shows that human impact on fire regimes is substantial and acts to limit the effect of climate in driving variation between years. This is in contrast to patterns in protected areas, where variation in accumulated rainfall and the length of the dry season influence the annual area burnt. Global models which assume strong links between fire and climate need to be re‐assessed in systems with high human impact.     

Generalized additive mixed models for disentangling long‐term trends,local anomalies,and seasonality in fruit tree phenology

Quantifying temporal patterns of ephemeral plant structures such as leaves, flowers, and fruits gives insight into both plant and animal ecology. Different scales of temporal changes in fruits, for example within‐ versus across‐year variability, are driven by different processes, but are not always easy to disentangle. We apply generalized additive mixed models (GAMMs) to study a long‐term fruit presence–absence data set of individual trees collected from a high‐altitude Afromontane tropical rain forest site within Bwindi Impenetrable National Park (BINP), Uganda. Our primary aim was to highlight and evaluate GAMM methodology, and quantify both intra‐ and interannual changes in fruit production. First, we conduct several simulation experiments to study the practical utility of model selection and smooth term estimation relevant for disentangling intra‐ and interannual variability. These simulations indicate that estimation of nonlinearity and seasonality is generally accurately identified using asymptotic theory. Applied to the empirical data set, we found that the forest‐level fruiting variability arises from both regular seasonality and significant interannual variability, with the years 2009–2010 in particular showing a significant increase in the presence of fruits‐driven by increased productivity of most species, and a regular annual peak associated occurring at the end of one of the two dry seasons. Our analyses illustrate a statistical framework for disentangling short‐term increases/decreases in fruiting effort while pinpointing specific times in which fruiting is atypical, providing a first step for assessing the impacts of regular and irregular (e.g., climate change) abiotic covariates on fruiting phenology. Some consequences of the rich diversity of fruiting patterns observed here for the population biology of frugivores in BINP are also discussed.     

Roles of Climate,Vegetation and Soil in Regulating the Spatial Variations in Ecosystem Carbon Dioxide Fluxes in the Northern Hemisphere

Climate, vegetation, and soil characteristics play important roles in regulating the spatial variation in carbon dioxide fluxes, but their relative influence is still uncertain. In this study, we compiled data from 241 eddy covariance flux sites in the Northern Hemisphere and used Classification and Regression Trees and Redundancy Analysis to assess how climate, vegetation, and soil affect the spatial variations in three carbon dioxide fluxes (annual gross primary production (AGPP), annual ecosystem respiration (ARE), and annual net ecosystem production (ANEP)). Our results showed that the spatial variations in AGPP, ARE, and ANEP were significantly related to the climate and vegetation factors (correlation coefficients, R = 0.22 to 0.69, P < 0.01) while they were not related to the soil factors (R = -0.11 to 0.14, P > 0.05) in the Northern Hemisphere. The climate and vegetation together explained 60 % and 58 % of the spatial variations in AGPP and ARE, respectively. Climate factors (mean annual temperature and precipitation) could account for 45 - 47 % of the spatial variations in AGPP and ARE, but the climate constraint on the vegetation index explained approximately 75 %. Our findings suggest that climate factors affect the spatial variations in AGPP and ARE mainly by regulating vegetation properties, while soil factors exert a minor effect. To more accurately assess global carbon balance and predict ecosystem responses to climate change, these discrepant roles of climate, vegetation, and soil are required to be fully considered in the future land surface models. Moreover, our results showed that climate and vegetation factors failed to capture the spatial variation in ANEP and suggest that to reveal the underlying mechanism for variation in ANEP, taking into account the effects of other factors (such as climate change and disturbances) is necessary.     

Range expansion through fragmented landscapes under a variable climate

Ecological responses to climate change may depend on complex patterns of variability in weather and local microclimate that overlay global increases in mean temperature. Here, we show that high‐resolution temporal and spatial variability in temperature drives the dynamics of range expansion for an exemplar species, the butterfly Hesperia comma. Using fine‐resolution (5 m) models of vegetation surface microclimate, we estimate the thermal suitability of 906 habitat patches at the species' range margin for 27 years. Population and metapopulation models that incorporate this dynamic microclimate surface improve predictions of observed annual changes to population density and patch occupancy dynamics during the species' range expansion from 1982 to 2009. Our findings reveal how fine‐scale, short‐term environmental variability drives rates and patterns of range expansion through spatially localised, intermittent episodes of expansion and contraction. Incorporating dynamic microclimates can thus improve models of species range shifts at spatial and temporal scales relevant to conservation interventions.     

Desertification alters patterns of aboveground net primary production in Chihuahuan ecosystems

Abstract The Chihuahuan desert of New Mexico, USA, has changed in historical times from semiarid grassland to desert shrublands dominated by Larrea tridentata and Prosopis glandulosa. Similar displacement of perennial grasslands by shrubs typifies desertification in many regions. Such structural vegetation change could alter average values of net primary productivity, as well as spatial and temporal patterns of production. We investigated patterns of aboveground plant biomass and net primary production in five ecosystem types of the Jornada Basin Long‐Term Ecological Research (LTER) site. Comparisons of shrub‐dominated desertified systems and remnant grass‐dominated systems allowed us to test the prediction that shrublands are more heterogeneous spatially, but less variable over time, than grasslands. We measured aboveground plant biomass and aboveground net primary productivity (ANPP) by species, three times per year for 10 years, in 15 sites of five ecosystem types (three each in Larrea shrubland, Bouteloua eriopoda grassland, Prosopis dune systems, Flourensia cernua alluvial flats, and grass‐dominated dry lakes or playas). Spatial heterogeneity of biomass at the scale of our measurements was significantly greater in shrub‐dominated systems than in grass‐dominated vegetation. ANPP was homogeneous across space in grass‐dominated systems, and in most growing seasons was significantly more patchy in shrub vegetation. Substantial interannual variability in ANPP complicates comparison of mean values across ecosystem types, but grasslands tended to support higher ANPP values than did shrub‐dominated systems. There were significant interactions between ecosystem type and season. Grasslands demonstrated higher interannual variation than did shrub systems. Desertification has apparently altered the seasonality of productivity in these systems; grasslands were dominated by summer growth, while sites dominated by Larrea or Prosopis tended to have higher spring ANPP. Production was frequently uncorrelated across sites of an ecosystem type, suggesting that factors other than season, regional climate, or dominant vegetation may be significant determinants of actual NPP.     

Effects of biotic and abiotic factors on the temporal dynamic of bat-fruit interactions

Mutualistic interactions between animals and plants vary over time and space based on the abundance of fruits or animals and seasonality. Little is known about this temporal dynamic and the influence of biotic and abiotic factors on the structure of interaction networks. We evaluated changes in the structure of network interactions between bats and fruits in relation to variations in rainfall. Our results suggest that fruit abundance is the main variable responsible for temporal changes in network attributes, such as network size, connectance, and number of interactions. In the same way, temperature positively affected the abundance of fruits and bats. An increase in temperature and alterations in rainfall patterns, due to human induced climate change, can cause changes in phenological patterns and fruit production, with negative consequences to biodiversity maintenance, ecological interactions, and ecosystem functioning.     

Mean conditions predict salt marsh plant community diversity and stability better than environmental variability

Environmental variability and the frequency of extreme events are predicted to increase in future climate scenarios; however, the role of fluctuations in shaping community composition, diversity and stability is not well understood. Identifying current patterns of association between measures of community stability and climatic means and variability will help elucidate the ways in which altered variability and mean conditions may change communities in the future. Salt marshes provide essential ecosystem services and are increasingly threatened by sea‐level rise, land‐use change, eutrophication and predator loss, yet the effects of temporal environmental variation on salt marshes remain unknown. We synthesized long‐term plant community monitoring data from 11 sites on both coasts of the United States. We used an information‐theoretic approach and linear models to determine the associations among long‐term mean conditions, interannual environmental variability, and plant community stability and diversity. We found that salt marsh community stability and diversity were more strongly related to long‐term means of temperature and precipitation than to interannual variation. Warm and wet environments had fewer species and less turnover among years. Our results suggest that communities in cool, dry environments may be more resilient to climate warming due to greater species richness and turnover. Mean conditions are sufficient to predict contemporary patterns of salt marsh plant community dynamics, but environmental variability may have stronger impacts as it increases with climate change.     

Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality

Accurate parameterization of rooting depth is difficult but important for capturing the spatio-temporal dynamics of carbon, water and energy cycles in tropical forests. In this study, we adopted a new approach to constrain rooting depth in terrestrial ecosystem models over the Amazon using satellite data [moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI)] and Biome-BGC terrestrial ecosystem model. We simulated seasonal variations in gross primary production (GPP) using different rooting depths (1, 3, 5, and 10 m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite-based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux-based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite-based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1–3 m) is sufficient in regions with a short dry season (e.g. 0–2 months), and deeper roots are required in regions with a longer dry season (e.g. 3–5 and 5–10 m for the regions with 3–4 and 5–6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth.     

Spatial and temporal variation of ecosystem properties at macroscales

Although spatial and temporal variation in ecological properties has been well‐studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation – spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.     

New insights into global patterns of ocean temperature anomalies: implications for coral reef health and management

Aim Coral reefs are widely considered to be particularly vulnerable to changes in ocean temperatures, yet we understand little about the broad‐scale spatio‐temporal patterns that may cause coral mortality from bleaching and disease. Our study aimed to characterize these ocean temperature patterns at biologically relevant scales. Location Global, with a focus on coral reefs. Methods We created a 4‐km resolution, 21‐year global ocean temperature anomaly (deviations from long‐term means) database to quantify the spatial and temporal characteristics of temperature anomalies related to both coral bleaching and disease. Then we tested how patterns varied in several key metrics of disturbance severity, including anomaly frequency, magnitude, duration and size. Results Our analyses found both global variation in temperature anomalies and fine‐grained spatial variability in the frequency, duration and magnitude of temperature anomalies. However, we discovered that even during major climatic events with strong spatial signatures, like the El Niño–Southern Oscillation, areas that had high numbers of anomalies varied between years. In addition, we found that 48% of bleaching‐related anomalies and 44% of disease‐related anomalies were less than 50 km², much smaller than the resolution of most models used to forecast climate changes. Main conclusions The fine‐scale variability in temperature anomalies has several key implications for understanding spatial patterns in coral bleaching‐ and disease‐related anomalies as well as for designing protected areas to conserve coral reefs in a changing climate. Spatial heterogeneity in temperature anomalies suggests that certain reefs could be targeted for protection because they exhibit differences in thermal stress. However, temporal variability in anomalies could complicate efforts to protect reefs, because high anomalies in one year are not necessarily predictive of future patterns of stress. Together, our results suggest that temperature anomalies related to coral bleaching and disease are likely to be highly heterogeneous and could produce more localized impacts of climate change.     

Latitudinal patterns of magnitude and interannual variability in net ecosystem exchange regulated by biological and environmental variables

Over the last two and half decades, strong evidence showed that the terrestrial ecosystems are acting as a net sink for atmospheric carbon. However the spatial and temporal patterns of variation in the sink are not well known. In this study, we examined latitudinal patterns of interannual variability (IAV) in net ecosystem exchange (NEE) of CO₂ based on 163 site-years of eddy covariance data, from 39 northern-hemisphere research sites located at latitudes ranging from ∼29°N to ∼64°N. We computed the standard deviation of annual NEE integrals at individual sites to represent absolute interannual variability (AIAV), and the corresponding coefficient of variation as a measure of relative interannual variability (RIAV). Our results showed decreased trends of annual NEE with increasing latitude for both deciduous broadleaf forests and evergreen needleleaf forests. Gross primary production (GPP) explained a significant proportion of the spatial variation of NEE across evergreen needleleaf forests, whereas, across deciduous broadleaf forests, it is ecosystem respiration (Re). In addition, AIAV in GPP and Re increased significantly with latitude in deciduous broadleaf forests, but AIAV in GPP decreased significantly with latitude in evergreen needleleaf forests. Furthermore, RIAV in NEE, GPP, and Re appeared to increase significantly with latitude in deciduous broadleaf forests, but not in evergreen needleleaf forests. Correlation analyses showed air temperature was the primary environmental factor that determined RIAV of NEE in deciduous broadleaf forest across the North American sites, and none of the chosen climatic factors could explain RIAV of NEE in evergreen needleleaf forests. Mean annual NEE significantly increased with latitude in grasslands. Precipitation was dominant environmental factor for the spatial variation of magnitude and IAV in GPP and Re in grasslands.     

Frequency of Extreme Heat Event as a Surrogate Exposure Metric for Examining the Human Health Effects of Climate Change

Epidemiological investigation of the impact of climate change on human health, particularly chronic diseases, is hindered by the lack of exposure metrics that can be used as a marker of climate change that are compatible with health data. Here, we present a surrogate exposure metric created using a 30-year baseline (1960–1989) that allows users to quantify long-term changes in exposure to frequency of extreme heat events with near unabridged spatial coverage in a scale that is compatible with national/state health outcome data. We evaluate the exposure metric by decade, seasonality, area of the country, and its ability to capture long-term changes in weather (climate), including natural climate modes. Our findings show that this generic exposure metric is potentially useful to monitor trends in the frequency of extreme heat events across varying regions because it captures long-term changes; is sensitive to the natural climate modes (ENSO events); responds well to spatial variability, and; is amenable to spatial/temporal aggregation, making it useful for epidemiological studies.     

Review article. Studying climate effects on ecology through the use of climate indices: the North Atlantic Oscillation, El Niño Southern Oscillation and beyond

Whereas the El Niño Southern Oscillation (ENSO) affects weather and climate variability worldwide, the North Atlantic Oscillation (NAO) represents the dominant climate pattern in the North Atlantic region. Both climate systems have been demonstrated to considerably influence ecological processes. Several other large-scale climate patterns also exist. Although less well known outside the field of climatology, these patterns are also likely to be of ecological interest. We provide an overview of these climate patterns within the context of the ecological effects of climate variability. The application of climate indices by definition reduces complex space and time variability into simple measures, ''packages of weather''. The disadvantages of using global climate indices are all related to the fact that another level of problems are added to the ecology-climate interface, namely the link between global climate indices and local climate. We identify issues related to: (i) spatial variation; (ii) seasonality; (iii) non-stationarity; (iv) nonlinearity; and (v) lack of correlation in the relationship between global and local climate. The main advantages of using global climate indices are: (i) biological effects may be related more strongly to global indices than to any single local climate variable; (ii) it helps to avoid problems of model selection; (iii) it opens the possibility for ecologists to make predictions; and (iv) they are typically readily available on Internet.