Temporal Scale Of Analysis Research Articles

Higher alpha and gamma, but not beta diversity in tropical than in Mediterranean temporary ponds: A multi‐taxon spatiotemporal approach

AbstractThe latitudinal diversity gradient predicts that tropical regions should have higher alpha, beta, and gamma diversity than temperate areas. However, only a few studies have assessed the temporal variability of the different components of diversity across climatic regions. In this study, we compare, using a spatial and temporal approach, the diversity of multiple taxa inhabiting tropical and Mediterranean temporary ponds. We sampled the biological communities of each set of ponds on three occasions during the same hydrological year. Under a spatial framework, we analyzed, alpha, beta, and gamma diversities. With a temporal approach, we compared the coefficients of variation in alpha diversity for each local community, and temporal beta diversity. Differences between regions and sampling periods were tested using generalized linear mixed models. We found higher gamma and alpha diversity in the tropical ponds, as expected given the latitudinal differences between them. However, phytoplankton and microinvertebrates from the Mediterranean region, matched or even exceeded tropical alpha diversity on some occasions. Spatial beta diversity did not differ between regions, and it showed lower values at the middle or the end of the hydroperiod in bacteria, micro‐ and macroinvertebrates and amphibians. Thus, processes homogenizing and heterogenising pond metacommunities must be balanced in both studied regions. Temporal variation in alpha and beta diversity was similar for ponds in both regions, except for macroinvertebrates and amphibians, suggesting differential effects on community variation observable only in animals with longer life‐spans, at our temporal scale of analysis.

Effect of survey parameters on unmanned aerial vehicles-derived topography for coastal dune monitoring

Coastal dunes are fragile ecosystems emerging at the interface between marine and continental environments. They provide multiple services, among which are the protection against the impact of storms and the hosting of diverse and unique species of fauna and flora. However, changes in the topography or biological component of these systems may endanger the perpetuation of service provision. Topographic changes within dunes can significantly differ in magnitude depending on the type of process (i.e., marine or aeolian) and the temporal scale of analysis (event to annual scale), making their monitoring a challenging task. In recent years, unmanned aerial vehicles (UAVs) have been increasingly used to monitor coastal dunes, proving to be a cost-efficient methodology for the collection of topographic data. Yet, the application of UAVs in combination with the structure from motion approach to obtain digital surface models (DSMs) presents some limitations related to the level of accuracy provided for the evaluation of topographical changes in dunes with low sedimentation rates. This work explores different survey configurations using UAVs flying at low altitudes with the aim of obtaining high-quality DSMs with vertical accuracies preferably around or lower than 0.04 m. Several tests were performed to evaluate the influence of different parameters on the accuracy of the DSM, including flight altitude and orientation, density and spatial distribution of ground control points (GCPs), terrain slope, vegetation cover, and sun-related parameters. The results indicate that the intended accuracies can be obtained by combining overlapped perpendicular flights, GCPs distributed regularly following a diamond grid, with densities of at least 6 GCPs per hectare, sun altitudes between 30 and 40 deg, and a total solar radiation per hour between 1750 and 2250 KJ / m2. In addition, better results were obtained across gentle slope areas, suggesting the eventual need to adapt to the particularities of each site to ensure the accuracy.

Disturbance has variable effects on the structural complexity of a temperate forest landscape

The temporal dynamics of forest canopy structure are influenced by disturbances that alter vegetation quantity and distribution. While canopy structural indicators such as leaf area index (LAI), canopy cover, and canopy height have been widely studied in the context of disturbance, the post-disturbance temporal dynamics of structural complexity, which summarizes the heterogeneity of vegetation arrangement, are poorly understood. With the goal of advancing conceptual and empirical understanding of the temporal dynamics of structural complexity following disturbance, we synthesized results from three large-scale disturbance manipulation experiments at the University of Michigan Biological Station (UMBS): the 4-year Forest Resilience Threshold Experiment (FoRTE) manipulating levels of disturbance severity; the decade-long Forest Accelerated Succession Experiment (FASET), in which all early successional tree species were stem-girdled within 39 ha in the same landscape; and forest chronosequences established following clear-cut harvesting. We found that the temporal dynamics of canopy structure following disturbance were dependent upon three factors: (1) the source and severity of disturbance; (2) the spatial and temporal scales of analysis; and (3) the measure of structure assessed. Unlike vegetation area index and canopy cover, which initially decreased in response to disturbance, structural complexity measures such as canopy and top rugosity did not consistently respond to moderate levels of disturbance severity. Over multi-decadal timescales, structural complexity increased to a maximum, regardless of whether fire occurred at the time of stand establishment, but intervening low-to-moderate severity disturbance in regrown century-old forests altered trajectories of canopy rugosity. We conclude that structural complexity indicators display a more nuanced temporal and directional response to disturbance than conventional leaf area and cover indexes. Predicting what disturbance conditions modify trajectories of structural complexity remains critical to disturbance characterization and the inference of ecosystem functioning.

Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods

The anatomical architecture of the brain constrains the dynamics of interactions between various regions. On a microscopic scale, neural plasticity regulates the connections between individual neurons. This microstructural adaptation facilitates coordinated dynamics of populations of neurons (mesoscopic scale) and brain regions (macroscopic scale). However, the mechanisms acting on multiple timescales that govern the reciprocal relationship between neural network structure and its intrinsic dynamics are not well understood. Studies empirically investigating such relationships on the whole-brain level rely on macroscopic measurements of structural and functional connectivity estimated from various neuroimaging modalities such as Diffusion-weighted Magnetic Resonance Imaging (dMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). dMRI measures the anisotropy of water diffusion along axonal fibers, from which structural connections are estimated. EEG and MEG signals measure electrical activity and magnetic fields induced by the electrical activity, respectively, from various brain regions with a high temporal resolution (but limited spatial coverage), whereas fMRI measures regional activations indirectly via blood oxygen level-dependent (BOLD) signals with a high spatial resolution (but limited temporal resolution). There are several studies in the neuroimaging literature reporting statistical associations between macroscopic structural and functional connectivity. On the other hand, models of large-scale oscillatory dynamics conditioned on network structure (such as the one estimated from dMRI connectivity) provide a platform to probe into the structure-dynamics relationship at the mesoscopic level. Such investigations promise to uncover the theoretical underpinnings of the interplay between network structure and dynamics and could be complementary to the macroscopic level inquiries. In this article, we review theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics. Special attention is given to various clinically relevant dimensions of brain connectivity such as the topological features and neural synchronization, and their applicability for a given modality, spatial or temporal scale of analysis is discussed. Our review provides a summary of the progress made along this line of research and identifies challenges and promising future directions for multi-modal neuroimaging analyses.

DST-Predict: Predicting Individual Mobility Patterns From Mobile Phone GPS Data.

Predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) is important to improve our understanding of the complexity of human mobility patterns, and to capture anomalous behaviors in an individual’s spatial movements, which can be particularly useful in situations such as those induced by the COVID-19 pandemic. We propose a system called Deep Spatio-Temporal Predictor (DST-Predict), that can predict the future visit frequency of an individual based on one’s past mobility behaviour patterns using GPS trace data collected from mobile phones. Predicting such spatial behavior is challenging, primarily because individuals’ patterns of location visits for each individual consists of both systematic and random components, which vary across the spatial and temporal scales of analysis. To address these issues, we propose a novel multi-view sequence-to-sequence model that uses Convolutional Long-short term memory (ConvLSTM) where the past history of frequent visit patterns features is used to predict individuals’ future visit patterns in a multi-step manner. Using the GPS survey data obtained from 1,464 participants in western New York, US, we demonstrated that the proposed system is capable of predicting individuals’ frequency of visits to common places in an urban setting, with high accuracy.

Assessing the relationship between vegetation greenness and surface temperature through Granger causality and Impulse-Response coefficients: a case study in Mexico

ABSTRACTThe existence of a consistent inverse relationship between vegetation greenness and surface temperature has been the premise of many environmental studies. However, some authors have found that the nature of this relationship varies depending on the spatial and temporal scales of analysis as well as vegetation type. Therefore, this research aims to contribute to the understanding of this matter by evaluating the annual and intra-seasonal relationship between Leaf Area Index (LAI) and Land Surface Temperature (LST) in Central Mexico using monthly anomalies of Moderate Resolution Imaging Spectroradiometer (MODIS) data collected from 2002 to 2017. LAI is proposed as an alternative index to overcome the shortcomings of other widely used indicators of vegetation greenness (e.g. Normalized Difference Vegetation Index and Enhanced Vegetation Index). The presence/absence of any relationship is investigated through the notion of Granger causality, while the sign and strength of the relationship is estimated by means of an Impulse-Response (IR) function. Unlike traditional regression and correlation analysis, the Granger causality approach enables the examination of lagged effects of one variable over the other based on past values of both variables. IR coefficients, which have been rarely used in the related literature, help to model the over-time response of a variable with the change of another variable. The overall results indicate that, at any temporal scale, Granger causality from LST to LAI occurs more consistently than causality in the opposite direction. At the annual scale, the nature of the relationship is primarily inverse in both directions and usually weaker from LAI to LST. At the seasonal scale, the occurrence of LST to LAI causality is higher in spring (it occurs in about 40% of the evaluated pixels) and lower in winter (10%) among all forest types. The effect of LST on LAI is predominantly inverse (median coefficient 1 month after impulse −0.043) and particularly strong in deciduous broadleaf forest during summer. On the other hand, the effect of LAI on LST is mainly direct in autumn and inverse in the remaining seasons, except for evergreen needleleaf forest where the effect is inverse only in summer. The highest presence (23%) and strength (−0.039) of LAI to LST causality occur in spring over deciduous broadleaf forest. Based on these results, caution has to be exercised when assuming a consistent strong inverse relationship between vegetation greenness and surface temperature, which seems to be the general consensus in much of the literature that makes use of these two variables to study an environmental phenomenon.

Infrastructures of moving water at the Maya site of Ucanal, Petén, Guatemala

It is increasingly common to conceptualize infrastructure not just as a built feature in the landscape, but as a shifting and entangled system that includes humans, different institutions and social groups, spiritual forces, and ecologies. These different aspects of infrastructure, however, are best identified at different temporal scales of analysis. Recent research at the Maya site of Ucanal in Petén, Guatemala, documents centrally managed water management features, such as canals and inverted causeways, that drain water away from the urban site core and into a nearby river, the Río Mopan. Their construction and use during the Terminal Classic period (ca. AD 830–1000), a period often associated with increasing aridity and drought, highlight the need to consider shorter temporal spans in which droughts were interspersed with hurricanes and periods of high precipitation. Furthermore, the consideration of even smaller temporal frames, on the order of annual dry-wet season cycles and daily practices, highlight the often overlooked aspects of ancient Maya water infrastructure systems: the labor necessary to maintain and repair canals and roads, deities or supernatural forces responsible for life-giving and life-taking rains, and the labor of common peoples who hauled water on a daily basis.

Functional trinity of public finance in an emerging economy

Two challenges in the literature have inspired this paper. First, the highly contradictory puzzle of debates on the “public budget–economic growth” correlation, explainable in great measure by the inadequacy of involved modeling tools (assumptions and specifications) with studied samples. Regarding this issue, the paper proposes three temporal scales of analysis: (1) the global historical trajectory of the modern society, concerning which the Wagner–Kaldor theorem has a leading explicative position; (2) the given structural state–society relationship, for which Barro–Armey–Rahn–Scully (BARS), Laffer in narrower sense (LINS), and the public budget balance restriction (BBR) curves are more suited; and (3) the short–medium horizon, where the Keynesian and post-Keynesian framework remains relevant. Another starting point of this paper refers to the second scale. While the BARS and LINS curves were extendedly examined, the BBR one benefited of attention only during the last period in the context of the sovereign debt crisis. Besides, all these functions were studied separately. Since in the real economy they interact continuously, we integrate BARS, LINS, and BBR curves into a unitary model with a “compatibility restriction” as technical solution. Data for an emergent medium-sized EU country (Romanian case study) illustrate this approach.

Effects of methodological decisions on rainfall-related crash relative risk estimates

Numerous studies have examined the influence of rainfall on the relative risk of crash, and they all agree that rainfall leads to an increase in relative risk as compared to dry conditions; what they do not agree on is the magnitude of these increases. Here we consider three methodological decisions made in computing the relative risk and examine their impacts: the inclusion or exclusion of zero total events (where no crashes occur during event or control periods), the temporal scale of analysis, and the use of information on pavement and weather conditions contained with the crash reports to determine relative risk. Our analyses are based on several years of data from six U.S. states (Arkansas, Georgia, Illinois, Maryland, Minnesota and Ohio). Zero total events in the context of weather related crash studies typically provide no information on the actual crash odds and greatly alter the distribution of relative risk estimates and should be removed from the analysis. While the use of a daily time step provides an estimate of relative risk that is not significantly different from an hourly time step for the majority of rural counties in our study area, the same is true of only 39% of the urban counties. Finally, the use of pavement and weather condition information from the crash reports results in relative risk estimates that are lower than the standard approach, however this difference decreases as rainfall totals increase. By highlighting the influence of methodological choices, we hope to pave the way towards the potential reduction in uncertainties in weather-related relative risk estimates.

Teaching Food History: A Discussion Among Practitioners

This roundtable discussion examines pedagogical practices in the emerging field of food history. Participants shared their experiences in a wide variety of different institutional settings and considered the differences between a food history classroom and more traditional history courses. Topics included how best to teach the history of taste, chronological and temporal scales of analysis, and issues of social justice. Discussants also problematized the epistemological limits of food history with particular focus on the classroom and the best way to provide educational outcomes that serve the future interest of students. Finally, the panel reflected on new directions for teaching food and how to expand individual classes into a larger curriculum.

A 184-year record of river meander migration from tree rings, aerial imagery, and cross sections

Channel migration is the primary mechanism of floodplain turnover in meandering rivers and is essential to the persistence of riparian ecosystems. Channel migration is driven by river flows, but short-term records cannot disentangle the effects of land use, flow diversion, past floods, and climate change. We used three data sets to quantify nearly two centuries of channel migration on the Powder River in Montana. The most precise data set came from channel cross sections measured an average of 21 times from 1975 to 2014. We then extended spatial and temporal scales of analysis using aerial photographs (1939–2013) and by aging plains cottonwoods along transects (1830–2014). Migration rates calculated from overlapping periods across data sets mostly revealed cross-method consistency. Data set integration revealed that migration rates have declined since peaking at 5m/year in the two decades after the extreme 1923 flood (3000m3/s). Averaged over the duration of each data set, cross section channel migration occurred at 0.81m/year, compared to 1.52m/year for the medium-length air photo record and 1.62m/year for the lengthy cottonwood record. Powder River peak annual flows decreased by 48% (201 vs. 104m3/s) after the largest flood of the post-1930 gaged record (930m3/s in 1978). Declining peak discharges led to a 53% reduction in channel width and a 29% increase in sinuosity over the 1939–2013 air photo record. Changes in planform geometry and reductions in channel migration make calculations of floodplain turnover rates dependent on the period of analysis. We found that the intensively studied last four decades do not represent the past two centuries.

Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach

This paper explores the opportunities for integrating Initiative Based Learning (IBL) and Integrated Assessment Models (IAMs) in order to improve our understanding of learning in the context of societal transition pathways, and more specifically by focusing on solar PV as an energy transition technology. Our analysis shows that IAMs and IBL conceptualize learning in a very different way, and the two approaches have major structural differences with respect to the geographical as well as the temporal scale of analysis. This is also due to the different goals of the two methodologies. The aim of IAM is to develop long-term energy and technology scenarios for the next thirty to eighty years, and to describe learning processes mostly to account for future potential improvements in technologies, while IBL focuses on understanding the configuration of actors in specific institutional settings that legitimize and support specific technologies and ultimately lead to dynamics of social learning. Although ambitious forms of integration between IAMs and IBL are not feasible today, the two approaches can be used in parallel and lead to mutual enrichment via a process that we label a two-way recursive collaboration.

Counterintuitive patterns of spawning stock age structure and recruitment in Lake Erie yellow perch (Perca flavescens)

Older spawning fish were often observed to contribute more recruits to harvested stocks than younger fish. However, effects of spawning stock age structure on recruitment were not universally detected. We tested effects of age-dependent absolute fecundity, age-dependent relative fecundity, and age-related maternal effects on recruitment by Lake Erie yellow perch (Perca flavescens). No effects of age-dependent absolute fecundity and age-dependent relative fecundity, nor positive age-related maternal effects, were detected at multidecadal or finer temporal scales of analysis, between 1975 and 2013. Instead, we found evidence of negative age-related maternal effects, of spatially and temporally varying strength; older cohorts in the spawning stock were sometimes associated with lower offspring survival. Such counterintuitive results imply that biotic and abiotic factors, such as interspecific predation, during larval and juvenile life stages might strongly affect recruitment of Lake Erie yellow perch.

Empirical Support for the Use of Prescribed Burning as a Fuel Treatment

Prescribed burning as a fuel treatment seeks to moderate wildfire impacts and decreases the areal extent of wildfires by increasing the effectiveness of fire suppression. Assessment of prescribed burning effectiveness is frequently anecdotal or based on simulation. This paper examines recent observational evidence of prescribed fire effectiveness. The spread rate and intensity of experimental fires in distinct fuel types have been shown to increase with time since treatment (fuel age) following fuel structure recovery. Prescribed fire constrains the size and especially the severity of individual fires, even under extremely severe weather conditions. At larger spatial and temporal scales of analysis, the effect of fuel age on unplanned fire severity is also evident, whether it comes from wildfires entering treated areas or from wildfires in fuel-reduced areas resulting from earlier wildfire occurrences. The persistence of these effects is variable, depending on vegetation type and productivity. The long-term reduction in wildfire area brought about by prescribed fire can be difficult to ascertain. Substantial effort (annual treatment rates >5 % of the landscape) has been shown to effectively control the extent of wildfires in forests, with 3–4 units of prescribed burning needed to reduce wildfire by one unit. Future studies should consider the decrease in area burned by high-severity fire as a more meaningful and objective measure of prescribed fire effectiveness than the decrease in wildfire area and should strive to document fire behaviour in treated versus untreated areas during wildfires.

Biodiversity–productivity relationship in ponds: Community and metacommunity patterns along time and environmental gradients

AbstractPrimary production correlates with diversity in various ways. These patterns may result from the interaction of various mechanisms related to the environmental context and the spatial and temporal scale of analysis. However, empirical evidence on diversity‐productivity patterns typically considers single temporal and spatial scales, and does not include the effect of environmental variables. In a metacommunity of macrophytes in ephemeral ponds, we analysed the diversity‐productivity relationship patterns in the field, the importance of the environmental variables of pond size and heterogeneity on such relationship, and the variation of these patterns at local (community level) and landscape scales (metacommunity level) across 52 ponds on twelve occasions, over five years (2005–2009). Combining all sampling dates, there were 377 ponds and 1954 sample‐unit observations. Vegetation biomass was used as a proxy for productivity, and biodiversity was represented by species richness, evenness, and their interaction. Environmental variables comprised pond area, depth and internal heterogeneity. Productivity and species richness were not directly related at the metacommunity level, and were positively related at the community level. Taking environmental variables into account revealed positive species richness‐productivity relationships at the metacommunity level and positive quadratic relationships at the community level. Productivity showed both positive and negative linear and nonlinear relationships with the size and heterogeneity of ponds. We found a weak relationship between productivity and evenness. The identity of variables associated with productivity changed between spatial scales and through time. The pattern of relationships between productivity and diversity depends on spatial scale and environmental context, and changes idiosyncratically through time within the same ecosystem. Thus, the diversity‐productivity relationship is not only a property of the study system, but also a consequence of environmental variations and the temporal and spatial scale of analysis.

Utility of marine benthic associations as a multivariate proxy of paleobathymetry: a direct test from recent coastal ecosystems of North Carolina.

Benthic marine fossil associations have been used in paleontological studies as multivariate environmental proxies, with particular focus on their utility as water depth estimators. To test this approach directly, we evaluated modern marine invertebrate communities along an onshore-offshore gradient to determine the relationship between community composition and bathymetry, compare the performance of various ordination techniques, and assess whether restricting community datasets to preservable taxa (a proxy for paleontological data) and finer spatial scales diminishes the applicability of multivariate community data as an environmental proxy. Different indirect (unconstrained) ordination techniques (PCoA, CA, DCA, and NMDS) yielded consistent outcomes: locality Axis 1 scores correlated with actual locality depths, and taxon Axis 1 scores correlated with actual preferred taxon depths, indicating that changes in faunal associations primarily reflect bathymetry, or its environmental correlatives. For datasets restricted to taxa with preservable hard parts, heavily biomineralized mollusks, open ocean habitats, and a single onshore-offshore gradient, the significant correlation between water depth and Axis 1 was still observed. However, for these restricted datasets, the correlation between Axis 1 and bathymetry was reduced and, in most cases, notably weaker than estimates produced by subsampling models. Consistent with multiple paleontological studies, the direct tests carried out here for a modern habitat using known bathymetry suggests that multivariate proxies derived from marine benthic associations may serve as a viable proxy of water depth. The general applicability of multivariate paleocommunity data as an indirect proxy of bathymetry is dependent on habitat type, intrinsic ecological characteristics of dominant faunas, taxonomic scope, and spatial and temporal scales of analysis, highlighting the need for continued testing in present-day depositional settings.

Characterizing air pollution patterns on multiple time scales in urban areas: a landscape ecological approach

Quantifying the spatiotemporal patterns of air pollution in urban areas is essential for studying ecological processes, environmental quality, and human health in cities. To adequately characterize or monitor air pollution patterns, one important issue is scale because the concentrations of air pollutants are temporally dynamic and spatially heterogeneous. Our research addresses the scale issue in air quality monitoring and analysis by considering the following research questions: (1) How does the spatial pattern of ozone change with the temporal scale of analysis? (2) How does the spatial pattern of PM10 change with the temporal scale of analysis? (3) What implications do these scale effects have for designing and evaluating air pollution monitoring networks? We systematically examined these questions based on data from official air pollution monitoring networks in the Phoenix metropolitan region, Arizona, USA. Our results showed that spatial patterns of both ozone and PM10 may change substantially with the temporal scale of analysis. Ozone patterns at broader (but not finer) temporal scales were more consistent across years, and exhibited a more uniform, regionalized pattern. PM10 patterns were less consistent across years than ozone, and exhibited a more localized effect. Spatial patterns of PM10 also varied seasonally. Our study demonstrates that it is critically important to consider the temporal and spatial scales in designing or evaluating air monitoring networks in particular and in conducting air pollution research in general.

Relating vegetation dynamics to temperature and precipitation at monthly and annual timescales in Taiwan using MODIS vegetation indices

To predict the responses of the timing, duration, and density of photosynthetically active plant cover to a changing climate, it is necessary to first quantitatively describe the relationships between temporal and spatial patterns of vegetation cover and both spatial and inter-annual variation in temperature and precipitation. We examined these relationships at multiple scales in Taiwan using monthly maximum composite values of MODIS-NDVI and MODIS-EVI between 2000 and 2012. The two vegetation indices were highly correlated to each other on a monthly basis for non-forest land-cover types, but correlated poorly in forests, probably due to the saturation of NDVI. However, the two indices were equally sensitive in detecting the onset and offset of growing season for all vegetation types. We found that EVI was positively related to both precipitation and temperature on a monthly timescale, although the relationships were not significant at the annual timescale. The much greater variation in monthly than in annual precipitation and temperature probably explains this difference. At low elevations, precipitation had a positive effect and temperature had a negative effect on EVI; however, at high elevations, which are mostly forested, both were positively related to EVI (although precipitation effects were not significant). We interpret this as evidence of water limitation of photosynthetic cover in the warmer, low-elevation parts of the island, whereas in the higher-elevation areas precipitation was usually adequate to satisfy evapotranspirative demand. This study illustrates the importance of examining the effects of precipitation and temperature on plant growth at a range of spatial and temporal scales. Specifically, finer spatial and temporal scales of analysis may better reveal climatic controls over vegetation growth than broader scales of analysis.

The impacts of tropical cyclones on the net carbon balance of eastern US forests (1851–2000)

In temperate forests of the eastern US, tropical cyclones are a principal agent of catastrophic wind damage, with dramatic impacts on the structure and functioning of forests. Substantial progress has been made to quantify forest damage and resulting gross carbon emissions from tropical cyclones. However, the net effect of storms on the carbon balance of forests depends not only on the biomass lost in single events, but also on the uptake during recovery from a mosaic of past events. This study estimates the net impacts of tropical cyclones on the carbon balance of US forests over the period 1851–2000. To track both disturbance and recovery and to isolate the effects of storms, a modeling framework is used combining gridded historical estimates of mortality and damage with a mechanistic model using an ensemble approach. The net effect of tropical cyclones on the carbon balance is shown to depend strongly on the spatial and temporal scales of analysis. On average, tropical cyclones contribute a net carbon source over latter half of the 19th century. However, throughout much of the 20th century a regional carbon sink is estimated resulting from periods of forest recovery exceeding damage. The large-scale net annual flux resulting from tropical cyclones varies by up to 50 Tg C yr−1, an amount equivalent to 17%–36% of the US forest carbon sink.

Elevation-Dependent Temperature Trends in the Rocky Mountain Front Range: Changes over a 56- and 20-Year Record

Determining the magnitude of climate change patterns across elevational gradients is essential for an improved understanding of broader climate change patterns and for predicting hydrologic and ecosystem changes. We present temperature trends from five long-term weather stations along a 2077-meter elevational transect in the Rocky Mountain Front Range of Colorado, USA. These trends were measured over two time periods: a full 56-year record (1953–2008) and a shorter 20-year (1989–2008) record representing a period of widely reported accelerating change. The rate of change of biological indicators, season length and accumulated growing-degree days, were also measured over the 56 and 20-year records. Finally, we compared how well interpolated Parameter-elevation Regression on Independent Slopes Model (PRISM) datasets match the quality controlled and weather data from each station. Our results show that warming signals were strongest at mid-elevations over both temporal scales. Over the 56-year record, most sites show warming occurring largely through increases in maximum temperatures, while the 20-year record documents warming associated with increases in maximum temperatures at lower elevations and increases in minimum temperatures at higher elevations. Recent decades have also shown a shift from warming during springtime to warming in July and November. Warming along the gradient has contributed to increases in growing-degree days, although to differing degrees, over both temporal scales. However, the length of the growing season has remained unchanged. Finally, the actual and the PRISM interpolated yearly rates rarely showed strong correlations and suggest different warming and cooling trends at most sites. Interpretation of climate trends and their seasonal biases in the Rocky Mountain Front Range are dependent on both elevation and the temporal scale of analysis. Given mismatches between interpolated data and the directly measured station data, we caution against an over-reliance on interpolation methods for documenting local patterns of climatic change.