Treefrogs in Forested Swamps at the La Selva Biological Station: Assemblage Variation through Space and Time

Swamp-breeding treefrogs form conspicuous components of many tropical forest sites, yet remain largely understudied. The La Selva Biological Station, a rainforest reserve in Costa Rica, harbors a rich swamp-breeding treefrog fauna that has been studied in only one of the many swamps found at the site. To understand if the species composition of treefrogs at La Selva varies over space or time, frogs were censused in 1982-83, 1994-95, 2005 and 2011 at two ponds located in the reserve. Data on treefrog habitat utilization were also collected. Species composition varied spatially only in 2011. Temporal variation was observed at both ponds for all groups tested. Habitat use varied among species and between swamps. The pattern of variation suggests that temporally dynamic systems such as temporary Neotropical forest swamps will converge and diverge in species composition over time.

Diversity and knowledge in the age of nation-building: Space and time in the thought of Yanagita Kunio

The study examines the thought of Yanagita Kunio (1875–1962), an influential Japanese nationalist thinker and a founder of an academic discipline named minzokugaku. The purpose of the study is to bring into light an unredeemed potential of his intellectual and political project as a critique of the way in which modern politics and knowledge systematically suppresses global diversity. The study reads his texts against the backdrop of the modern understanding of space and time and its political and moral implications and traces the historical evolution of his thought that culminates in the establishment of minzokugaku. My reading of Yanagita’s texts draws on three interpretive hypotheses. First, his thought can be interpreted as a critical engagement with John Stuart Mill’s philosophy of history, as he turns Mill’s defense of diversity against Mill’s justification of enlightened despotism in non-Western societies. Second, to counter Mill’s individualistic notion of progressive agency, he turns to a Marxian notion of anthropological space, in which a laboring class makes history by continuously transforming nature, and rehabilitates the common people (jomin) as progressive agents. Third, in addition to the common people, Yanagita integrates wandering people as a countervailing force to the innate parochialism and conservatism of agrarian civilization. To excavate the unrecorded history of ordinary farmers and wandering people and promote the formation of national consciousness, his minzokugaku adopts travel as an alternative method for knowledge production and political education. In light of this interpretation, the aim of Yanagita’s intellectual and political project can be understood as defense and critique of the Enlightenment tradition. Intellectually, he attempts to navigate between spurious universalism and reactionary particularism by revaluing diversity as a necessary condition for universal knowledge and human progress. Politically, his minzokugaku aims at nation-building/globalization from below by tracing back the history of a migratory process cutting across the existing boundaries. His project is opposed to nation-building from above that aims to integrate the world population into international society at the expense of global diversity.

Quasi-Orthogonal Space-Time Block Coding Using Polynomial Phase Modulation

Recently, polynomial phase modulation (PPM) was shown to be a power- and bandwidth-efficient modulation format. These two characteristics are in high demand nowadays specially in mobile applications, where devices with size, weight, and power (SWaP) constraints are common. In this paper, we propose implementing a full-diversity quasiorthogonal space-time block code (QOSTBC) using polynomial phase signals as modulation format. QOSTBCs along with PPM are used in order to improve the power efficiency of communication systems with four transmit antennas. We obtain the optimal PPM constellations that ensure full diversity and maximize the QOSTBC's minimum coding gain distance. Simulation results show that by using QOSTBCs along with a properly selected PPM constellation, full diversity in flat fading channels and thus low BER at high signal-to-noise ratios (SNR) can be ensured. More importantly, it is also shown that QOSTBCs using PPM achieve a better error performance than those using conventional modulation formats.

Space-for-Time Substitution Works in Everglades Ecological Forecasting Models

Space-for-time substitution is often used in predictive models because long-term time-series data are not available. Critics of this method suggest factors other than the target driver may affect ecosystem response and could vary spatially, producing misleading results. Monitoring data from the Florida Everglades were used to test whether spatial data can be substituted for temporal data in forecasting models. Spatial models that predicted bluefin killifish (Lucania goodei) population response to a drying event performed comparably and sometimes better than temporal models. Models worked best when results were not extrapolated beyond the range of variation encompassed by the original dataset. These results were compared to other studies to determine whether ecosystem features influence whether space-for-time substitution is feasible. Taken in the context of other studies, these results suggest space-for-time substitution may work best in ecosystems with low beta-diversity, high connectivity between sites, and small lag in organismal response to the driver variable.

Space-for-Time Substitution Works in Everglades Ecological Forecasting Models

Space-for-time substitution is often used in predictive models because long-term time-series data are not available. Critics of this method suggest factors other than the target driver may affect ecosystem response and could vary spatially, producing misleading results. Monitoring data from the Florida Everglades were used to test whether spatial data can be substituted for temporal data in forecasting models. Spatial models that predicted bluefin killifish (Lucania goodei) population response to a drying event performed comparably and sometimes better than temporal models. Models worked best when results were not extrapolated beyond the range of variation encompassed by the original dataset. These results were compared to other studies to determine whether ecosystem features influence whether space-for-time substitution is feasible. Taken in the context of other studies, these results suggest space-fortime substitution may work best in ecosystems with low beta-diversity, high connectivity between sites, and small lag in organismal response to the driver variable.

Travel times in the vadose zone: Variability in space and time

Peer reviewed

Elucidating the Space-Time Structure of Low Level Warm Season Precipitation Processes in the Southern Appalachian Mountains Using Models and Observations

Light rainfall is the baseline input to the annual water budget in mountainous landscapes through the tropics and at mid-latitudes. In the Southern Appalachians, the contribution from light rainfall ranges from 50-60% during wet years to 80-90% during dry years, with convective activity and tropical cyclone input providing most of the interannual variability. The Southern Appalachians is a region characterized by rich biodiversity that is vulnerable to land use/land cover changes due to its proximity to a rapidly growing population. Persistent near surface moisture and associated microclimates observed in this region has been well documented since the colonization of the area in terms of species health, fire frequency, and overall biodiversity. The overarching objective of this research is to elucidate the microphysics of light rainfall and the dynamics of low level moisture in the inner region of the Southern Appalachians during the warm season, with a focus on orographically mediated processes. The overarching research hypothesis is that physical processes leading to and governing the life cycle of orographic fog, low level clouds, and precipitation, and their interactions, are strongly tied to landform, land cover, and the diurnal cycles of flow patterns, radiative forcing, and surface fluxes at the ridge-valley scale. The following science questions will be addressed specifically: 1) How do orographic clouds and fog affect the hydrometeorological regime from event to annual scale and as a function of terrain characteristics and land cover?; 2) What are the source areas, governing processes, and relevant time-scales of near surface moisture convergence patterns in the region?; and 3) What are the four dimensional microphysical and dynamical characteristics, including variability and controlling factors and processes, of fog and light rainfall? The research was conducted with two major components: 1) ground-based high-quality observations using multi-sensor platforms and 2) interpretive numerical modeling guided by the analysis of the in situ data collection. Findings illuminate a high level of spatial – down to the ridge scale - and temporal – from event to annual scale - heterogeneity in observations, and a significant impact on the hydrological regime as a result of seeder-feeder interactions among fog, low level clouds, and stratiform rainfall that enhance coalescence efficiency and lead to significantly higher rainfall rates at the land surface. Specifically, results show that enhancement of an event up to one order of magnitude in short-term accumulation can occur as a result of concurrent fog presence. Results also show that events are modulated strongly by terrain characteristics including elevation, slope, geometry, and land cover. These factors produce interactions between highly localized flows and gradients of temperature and moisture with larger scale circulations. Resulting observations of DSD and rainfall patterns are stratified by region and altitude and exhibit clear diurnal and seasonal cycles.

Historical Space Steps of Turkey: It Is High Time to Establish the Turkish Space Agency

This paper discusses the importance of space in today’s space driven world, the current space activities of Turkey, its space organizations with legislation background information and calls for the necessity for the establishment of the Turkish Space Agency (TSA). Firstly, the importance of space is given which is followed by a brief background and current space activities in Turkey. Then, the answers to why Turkey needs a National Space Agency are outlined by stating its expected role and duties. Additionally, the framework for space policy for Turkey is proposed and the findings are compared with other developing regional space actors. Lastly, it is proposed and demonstrated that Turkey is on the right track with its space policy and it is suggested that the establishment of the TSA is critical both for a coherent space policy and progress as well as the successful development of its national space industry, security and international space relations.

Integrated Monitoring of Mola mola Behaviour in Space and Time

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of fine-scale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) video-recorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (rs = 0.184, p<0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator’s fine-scale behaviour observed over a two weeks in May 2014.

Integrated Monitoring of Mola mola Behaviour in Space and Time

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of fine-scale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) video-recorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (rs = 0.184, p<0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator’s fine-scale behaviour observed over a two weeks in May 2014.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.

A space-time hybrid hourly rainfall model for derived flood frequency analysis

For derived flood frequency analysis based on hydrological modelling long continuous precipitation time series with high temporal resolution are needed. Often, the observation network with recording rainfall gauges is poor, especially regarding the limited length of the available rainfall time series. Stochastic precipitation synthesis is a good alternative either to extend or to regionalise rainfall series to provide adequate input for long-term rainfall-runoff modelling with subsequent estimation of design floods. Here, a new two step procedure for stochastic synthesis of continuous hourly space-time rainfall is proposed and tested for the extension of short observed precipitation time series. First, a single-site alternating renewal model is presented to simulate independent hourly precipitation time series for several locations. The alternating renewal model describes wet spell durations, dry spell durations and wet spell intensities using univariate frequency distributions separately for two seasons. The dependence between wet spell intensity and duration is accounted for by 2-copulas. For disaggregation of the wet spells into hourly intensities a predefined profile is used. In the second step a multi-site resampling procedure is applied on the synthetic point rainfall event series to reproduce the spatial dependence structure of rainfall. Resampling is carried out successively on all synthetic event series using simulated annealing with an objective function considering three bivariate spatial rainfall characteristics. In a case study synthetic precipitation is generated for some locations with short observation records in two mesoscale catchments of the Bode river basin located in northern Germany. The synthetic rainfall data are then applied for derived flood frequency analysis using the hydrological model HEC-HMS. The results show good performance in reproducing average and extreme rainfall characteristics as well as in reproducing observed flood frequencies. The presented model has the potential to be used for ungauged locations through regionalisation of the model parameters.

ANALYSIS OF MACROINVERTEBRATE COMMUNITIES IN SEASONAL WETLANDS THROUGH TIME, ACROSS SPACE, AND USING SPECIES TRAITS

Restoration of natural wetlands may be informed by macroinvertebrate community composition. Macroinvertebrate communities of wetlands are influenced by environmental characteristics such as vegetation, soil, hydrology, land use, and isolation. This dissertation explores multiple approaches to the assessment of wetland macroinvertebrate community composition, and demonstrates how these approaches can provide complementary insights into the community ecology of aquatic macroinvertebrates. Specifically, this work focuses on macroinvertebrates of Delmarva Bays, isolated seasonal wetlands found on Maryland’s eastern shore. A comparison of macroinvertebrate community change over a nine years in a restored wetland complex indicated that the macroinvertebrate community of a rehabilitated wetlands more rapidly approximated the community of a reference site than did a newly created wetland. The recovery of a natural macroinvertebrate community in the rehabilitated wetland indicated that wetland rehabilitation should be prioritized over wetland creation and long-term monitoring may be needed to evaluate restoration success. This study also indicated that characteristics of wetland vegetation reflected community composition. The connection between wetland vegetation and macroinvertebrate community composition led to a regional assessment of predaceous diving beetle (Coleoptera: Dytiscidae) community composition in 20 seasonal wetlands, half with and half without sphagnum moss (Sphagnum spp.). Species-level identifications indicated that wetlands with sphagnum support unique and diverse assemblages of beetles. These patterns suggest that sphagnum wetlands provide habitat that supports biodiversity on the Delmarva Peninsula. To compare traits of co-occurring beetles, mandible morphology and temporal and spatial variation were measured between three species of predaceous diving beetles. Based on mandible architecture, all species may consume similarly sized prey, but prey characteristics likely differ in terms of piercing force required for successful capture and consumption. Therefore, different assemblages of aquatic beetles may have different effects on macroinvertebrate community structure. Integrating community-level and species-level data strengthens the association between individual organisms and their ecological role. Effective restoration of imperiled wetlands benefits from this integration, as it informs the management practices that both preserve biodiversity and promote ecosystem services.