Sacred fig trees promote frugivore visitation and tree seedling abundance in South India

While sacred groves (forest fragments protected for religious reasons) are widely acknowledged to have a beneficial effect on biodiversity conservation, the ecological benefits of individual sacred trees remain unknown. Fig trees are present as sacred trees in humandominated landscapes across South Asia and are considered keystone species for wildlife in tropical forests. If frugivores continue to visit fig trees in disturbed landscapes, they may deposit seeds of other tree species beneath fig canopies, ultimately facilitating forest regeneration. We studied whether sacred fig trees in Tamil Nadu, India can facilitate seed dispersal in human-dominated landscapes. We quantified abundance of sacred fig trees at the study site, assessed whether seed-dispersing frugivore visitation to fig trees is affected by human disturbance, and compared tree seedling density beneath fig trees and open areas. We found that some species of frugivorous birds and bats will visit large fig trees in conditions of high human disturbance and that tree seedling density is significantly higher under sacred trees compared to open areas. By promoting frugivore activity, sacred fig trees may have a beneficial effect on biodiversity conservation in human-dominated landscapes

Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion

The impact of premarital cycling on early marriage

Using a sample of 564 newlywed couples and the enduring dynamics model of marriage (Caughlin, Huston, & Houts, 2000), we examined the impact of premarital cycling (breaking up and renewing) on the entrance into marriage and relationship dynamics over the first five years. Consistent with the enduring dynamics model, results demonstrated cyclical couples (compared to non-cyclical couples) exhibited worse adjustment on a variety of relationship indicators at the entrance to marriage and were more likely to experience a trial separation over the first five years. Dyadic parallel process growth curve analysis further revealed that premarital cycling predicted lower initial relationship satisfaction that was sustained over the first five years of marriage. Implications for theory, research, and intervention with premarital couples are discussed. These results provide evidence that courtships characterized by breakups and renewals represent a relational vulnerability with negative implications extending years into the future

Identifying malaria transmission foci for elimination using human mobility data

Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model

Pulsation modes in rapidly rotating stellar models based on the Self-Consistent Field method

Context: New observational means such as the space missions CoRoT and Kepler and ground-based networks are and will be collecting stellar pulsation data with unprecedented accuracy. A significant fraction of the stars in which pulsations are observed are rotating rapidly. Aims: Our aim is to characterise pulsation modes in rapidly rotating stellar models so as to be able to interpret asteroseismic data from such stars. Methods: The pulsation code developed in Ligni\`eres et al. (2006) and Reese et al. (2006) is applied to stellar models based on the self-consistent field (SCF) method (Jackson et al. 2004, 2005, MacGregor et al. 2007). Results: Pulsation modes in SCF models follow a similar behaviour to those in uniformly rotating polytropic models, provided that the rotation profile is not too differential. Pulsation modes fall into different categories, the three main ones being island, chaotic, and whispering gallery modes, which are rotating counterparts to modes with low, medium, and high l-|m| values, respectively. The frequencies of the island modes follow an asymptotic pattern quite similar to what was found for polytropic models. Extending this asymptotic formula to higher azimuthal orders reveals more subtle behaviour as a function of m and provides a first estimate of the average advection of pulsation modes by rotation. Further calculations based on a variational principle confirm this estimate and provide rotation kernels that could be used in inversion methods. When the rotation profile becomes highly differential, it becomes more and more difficult to find island and whispering gallery modes at low azimuthal orders. At high azimuthal orders, whispering gallery modes, and in some cases island modes, reappear.Comment: 16 pages, 11 figures, accepted for publication in A&

The Message Design Logics of Responses to HIV Disclosures

This article uses the theory of message design logics to investigate the relative sophistication of responses to disclosure of HIV status. In Study 1, 548 college students imagined a sibling revealing an HIV-positive diagnosis. Their responses to the HIV disclosures were coded as expressive (n= 174), conventional (n= 298), or rhetorical (n= 66). Type of message produced was associated with gender and HIV aversion. In Study 2, 459 individuals living with HIV rated response messages that were taken verbatim from Study 1. Expressive messages were rated lowest in quality, and rhetorical messages were rated highest. The discussion focuses on the utility of message design logics for understanding responses to HIV disclosures and the implications for message design logics

Post-Fire Seed Dispersal of a Wind-Dispersed Shrub Declined with Distance to Seed Source, yet had High Levels of Unexplained Variation

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m−2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes

Dynamic Job Satisfaction Shifts: Implications for Manager Behavior and Crossover to Employees

In this dissertation, I investigated job satisfaction from a dynamic perspective. Specifically, I integrated the momentum model of job satisfaction with the affective shift model and crossover theory in an effort to move beyond traditional, static conceptions of job satisfaction and other constructs. Recent research and theoretical development has focused on the meaning of job satisfaction change for workers and how such change impacts their decisions to leave an organization. To extend this line of inquiry, I posited hypotheses pertaining to: (a) job satisfaction change with respect to positive work behavior (i.e., organizational citizenship behavior, family-supportive supervisor behavior); (b) the potential moderating effect of changes in negative work events (i.e., job demands, interpersonal conflict) on the relation between job satisfaction change and turnover intentions change and positive work behavior; and (c) the crossover of job satisfaction change from managers to employees and the potential underlying behavioral mechanisms. An archival dataset collected by the Work, Family & Health Network was used to investigate the aforementioned phenomena. Data were collected at two time points with a six-month interval via face-to-face computer-assisted personal interviews from individuals working at 30 facilities from a U.S. extended-healthcare organization. In total, data from 184 managers and 1,524 of their employees were used to test hypotheses. Data were analyzed using multilevel structural equation modeling. In an extension of the momentum model, I found that managers’ job satisfaction change positively related to changes in employee reports of their FSSB; in addition, I replicated prior findings in which job satisfaction change negatively related to turnover intentions change. Furthermore, based on my integration of the momentum model and the affective shift model, I tested the proposition that changes in negative work events (i.e., job demands, interpersonal conflict) would moderate the relationship between changes in job satisfaction and focal outcomes. For certain operationalizations of negative work events, hypothesis testing revealed significant interactions with respect to changes in all three outcomes: turnover intentions, OCB, and FSSB. The form of the interactions, however, deviated from my predictions for models including changes in turnover intentions and OCB, although my predictions were supported for models including changes in FSSB. In my integration of the momentum model and crossover theory, the associated hypotheses were met with very limited support. Specifically, the relationship between managers\u27 job satisfaction change and employees\u27 job satisfaction change approached significance, but the relationship between managers\u27 level of job satisfaction and their employees\u27 subsequent level of job satisfaction did not receive support. Similarly, the proposed mediational mechanisms (i.e., managers\u27 OCB and FSSB) of these crossover relations went unsupported. In sum, while my contributions to the momentum model and the affective shift model were notable, my proposed integration of the momentum model and crossover theory was met with limited support. Overall, findings from this dissertation yield important implications for both theory and practice, as they may draw more attention to changes in job satisfaction, as well as the potentially beneficial role of changes in perceived negative work events

Applied Soft Classes and Fuzzy Confusion in a Patchwork Semi-Arid Ecosystem: Stitching Together Classification Techniques to Preserve Ecologically-Meaningful Information

Dryland ecosystems have complex vegetation communities, including subtle transitions between communities and heterogeneous coverage of key functional groups. This complexity challenges the capacity of remote sensing to represent land cover in a meaningful way. Many remote sensing methods to map vegetation in drylands simplify fractional cover into a small number of functional groups that may overlook key ecological communities. Here, we investigate a remote sensing process that further advances our understanding of the link between remote sensing and ecologic community types in drylands. We propose a method using k-means clustering to establish soft classes of vegetation cover communities from detailed field observations. A time-series of Sentinel-2 satellite imagery and a random forest classification leverages the mixing of different phenologies over time to impute such soft community classes over the landscape. Next, we discuss the advantages of using a fuzzy confusion approach for soft classes in cases such as understanding subtle transitions in ecotones, identifying areas for targeted remediation or treatment, and in ascertaining the spatial distribution of non-dominant covers such as biological soil crusts and small native bunchgrasses which have typically been difficult to map with traditional remote sensing classifications. Our pixel-level analysis is relevant to the scale of management decisions and represents the complexity of the landscape. The combination of cloud computing with the spatial, temporal, and spectral observations from Sentinel-2 allow us to develop these ecologically-meaningful observations at large spatial extents, including complete coverage at landscape scales. Re-interpretation of large extent maps of soft classes may be helpful to land managers who need community-level information for fuel breaks, restoration, invasive plant suppression, or habitat identification