Improvement of Global Change Projections for Riverine Benthic Macroinvertebrates

Species’ distribution models (SDMs) are predictive models that are increasingly applied to river ecosystems as a complement to large scale observational analyses. Current ecological theory on river discharge acknowledges that hydrological flow regime is one of the most important drivers of lotic systems, influencing the abundance and distribution of river biota. However, few studies on stream SDMs incorporate specific data describing flow regime, with most studies only including data describing climate or river related surrogates. These hydrological variables have a significant impact however, they only partially represent the critical aspects of flow regime. This limitation is partially due to available hydrological data, which are often limited in their spatio-temporal extent and resolution for use in SDMs. Another major challenge in SDM studies is the selection of relevant environmental predictors, particularly when modeling large communities. Often, variable choice is made for an entire community and not for specific species, resulting in inappropriate predictors for at least some species, and affecting model performance and predicted distributions. This thesis is method based, and my main goal was to advance the predictive ability of SDMs for riverine benthic macroinvertebrates by integrating hydrological predictors that describe flow regime. The thesis is divided into three parts: First, I developed a high resolution spatio-temporal dataset of streamflow, and a set of hydrological metrics for the German stream network. Second, I proposed a variable selection method to select the optimal environmental variables for use in SDMs, and I investigated the impact of predictor set choice in SDMs. Third, I disentangled the role of hydrology in SDMs by investigating the influence of climate and hydrology related datasets. Using empirical streamflow data from gauging stations across Germany and modeled seasonal accumulated precipitation, I applied a weighted linear regression model to predict a continuous daily time series of streamflow (m3 s-1) spanning 64 years (1950-2013). The daily streamflow data were subsequently applied as input to successfully calculate 53 Indicators of Hydrologic Alteration (IHA), which describe the magnitude, frequency, duration, timing and change rate of high, low and average streamflow conditions. I performed temporal and spatial validations on the streamflow data, through which I confirmed that the predicted flow data are adequate for use in predictive ecological models. Both the IHA metrics and the streamflow datasets are available open access for use in predictive models. By applying the IHA metrics, together with data describing climate, land-use, and topography in Boosted Regression Trees (BRTs), I created two predictor sets 1) a species-specific predictor set for each individual species and 2) a uniform predictor set for the community as a whole. Through this procedure, I highlighted a useful and effective method to impartially select highly relevant environmental variables. To investigate the impact of each predictor set on predictive ability, I applied SDMs on a community of macroinvertebrates. The SDMs rendered 10 species where the models increased in accuracy (Mean TSS = 0.59 ± 0.03) and 10 species where the models decreased in accuracy (Mean TSS =0.49 ± 0.04) with the species-specific predictor set. The 20 species, showed distinct differences in terms of their ecological traits, known occurrences, and preferred environmental conditions. To investigate the separate influence of climate and hydrology, I calibrated SDMs on a community of macroinvertebrates with three datasets describing either 1) climate only (bioclimate), 2) hydrology only (hydrology) and 3) information on both climate and hydrology (hydroclimate), in four model configurations. SDMs applied with bioclimate and hydrology, performed significantly better overall (Mean TSS = 0.68 ± 0.02), exhibited the lowest unexplained variance (0.29), and predicted significantly larger range sizes (Mean no. of presences; 3482.6 ± 129.1). I found bioclimate to be the most important individual factor for species’ distributions in terms of both variable importance and proportional explained variance. Despite the importance of bioclimate, hydrology contributed to a higher proportion of explained variance, unrivalled by other SDM configurations. The larger predicted range sizes may be due to the better description of the river discharge regime provided by the hydrological variables. Through this thesis, I have created and integrated hydrological variables in SDMs, as well as developed and validated effective methods to improve prediction performance of riverine species’ distribution to advance freshwater SDM research. The introduced methods can be applied in different geographical regions as well as under alternative time periods and spatial scales. Due to the implications associated with altered model accuracy and predicted range size, applying SDMs with hydrological variables has the potential to aid river management decisions and conservation efforts.Artverbreitungsmodelle (eng.: species distribution models; SDMs) werden zunehmend für Flussökosysteme angewandt um groß-skalige Analysen zu ergänzen. In der aktuellen ökologischen Theorie wird das Abflussverhalten als einer der wesentlichen Einflussfaktoren für das Vorkommen und die Verbreitung von Flusslebewesen beschrieben. Es gibt jedoch nur wenige Studien zur Modellierung der Verbreitung von Fließgewässerarten, die Daten berücksichtigen, die das Abflussverhalten detailliert beschreiben. Anstelle dessen, werden häufig Klimadaten, oder aber indirekte Indikatoren genutzt. Derartige indirekte hydrologische Indikatoren haben zwar einen großen Einfluss auf die Verbreitung von Fließgewässerarten, dennoch können sie die wesentlichen Faktoren des Abflussverhaltens nur teilweise abbilden. Dieses Vorgehen ist teilweise auf die Verfügbarkeit von geeigneten hydrologischen Daten für SDMs zurückzuführen, da diese meist in ihrer räumlichen und zeitlichen Ausdehnung und Auflösung limitiert sind. Eine weitere Herausforderung in der Anwendung von SDMs ist die Auswahl relevanter Umwelt-Prädiktoren bei der Modellierung großer Artgemeinschaften, da diese Entscheidung zumeist für die gesamte Artgemeinschaft vorgenommen wird und entsprechend nicht artspezifisch ist. Dies führt dazu, dass die Prädiktoren für einige Arten ungeeignet sind, was wiederum die Modellgüte und die vorhergesagten Verbreitungsmuster beeinflusst. Das Hauptziel der vorliegenden methodischen Arbeit ist es, die Vorhersagekapazitäten von SDMs für benthische Makroinvertebraten durch Einbindung von hydrologischen Prädiktoren, die das Abflussverhalten beschreiben, zu verbessern. Die Arbeit besteht aus drei Teilen. Im ersten Teil habe ich einen zeitlich und räumlich (1 km2) hoch aufgelösten Datensatz, der den Abfluss und eine Reihe weiterer hydrologischer Einflussgrößen beinhaltet, für Deutschland entwickelt. Im zweiten Teil habe ich eine Methode zur Ermittlung der optimalen Prädiktoren für den Einsatz in SDMs entwickelt und den Effekt der Auswahl der Prädiktoren auf SDMs untersucht. Im dritten Teil geht es um die Rolle der Hydrologie in SDMs, die ich über den Einfluss von klimatischen und hydrologischen Datensätzen untersucht habe. Auf der Grundlage von deutschlandweit gemessenen Abflussdaten und modellierten Niederschlagsdaten, habe ich mittels gewichteter linearer Regression deutschlandweite tägliche Abflussdaten (m3 s-1) für einen Zeitraum von 64 Jahren (1950 bis 2013) erstellt. Im Anschluss wurden diese täglichen Abflussdaten verwendet, um 53 Indikatoren der hydrologischen Veränderung (IHA) zu berechnen, die die Stärke, Frequenz, Dauer, und Größe der Veränderung von Hoch- Niedrig-und Mittelwasser Ereignissen beschreiben. Die Abflussdaten wurden zeitlich und räumlich validiert, wodurch ich erfolgreich zeigen konnte, dass die modellierten IHA für SDMs genutzt werden können. Sowohl die IHA, als auch die modellierten Abflussdaten sind öffentlich verfügbar und können so für SDMs genutzt werden. Unter Anwendung der modellierten IHA sowie der Klima-, Landnutzungs-, und topografischen Prädiktoren wurden zwei separate Sets an Prädiktoren mit Hilfe von Boosted Regression Trees (BRTs) erstellt. Ein Set war dabei artspezifisch (für jede der Arten individuell), das zweite Set war ein uniformes Set (für alle Arten gleich). Mit diesem Ansatz konnte ich die Anwendbarkeit und Effektivität der Methode aufzeigen, die eine Auswahl von Prädiktoren für individuelle Arten ermöglicht. Um den Effekt der unterschiedlichen Sets an Prädiktoren auf die Vorhersagekapazität zu untersuchen wurden diese auf eine Makroinvertebratengemeinschaft angewendet. Die individuellen Sets an Prädiktoren resultierten in einer deutlichen Verbesserung der Modellgüte für 10 der modellierten Arten (Mean TSS = 0.59 ± 0.03). Für 10 weitere Arten wurde allerdings eine deutliche Verschlechterung der Modellgüte aufgezeigt (Mean TSS =0.49 ± 0.04). Diese 20 Arten weisen sehr deutliche Unterschiede in Bezug auf ihre Traits, Vorkommenspunkte und die bevorzugten Habitateigenschaften auf. Um die Einzeleffekte von Klima und Hydrologie auf SDMs und deren Vorhersagen abzuschätzen, habe ich für eine Makroinvertebratengemeinschaft drei verschiedene Sets von Prediktoren 1.) nur Klima, 2.) nur Hydrologie und 3.) eine Kombination aus Klima und Hydrologie in vier verschiedenen Konfigurationen untersucht. SDMs die mit einer Kombination aus nur klimatischen und nur hydrologischen Prediktoren kalibriert wurden, wiesen eine signifikant bessere Modellgüte (Mittlerer TSS = 0.68 ± 0.02) auf, hatten die kleinste unerklärte Varianz (0.29) und haben signifikant größere Verbreitungsgebiete für die einzelnen Arten vorhergesagt (Mittlere Anzahl der vorhergesagten Vorkommenspunkte 3482.6 ± 129.1). Sowohl, hinsichtlich der relativen Bedeutung der Prädiktoren, als auch in Bezug auf die erklärte Varianz in den Modellen, haben sich reine Klimaprädiktoren als wichtigste Einflussgrößen für die Modellierung der Verbreitungsgebiete der Makroinvertebraten herausgestellt. Neben der großen Bedeutung von Klimaprädiktoren zeigte sich, dass hydrologische Prädiktoren im Allgemeinen einen höheren Anteil zur erklärten Varianz beigetragen haben. Die größeren vorhergesagten Verbreitungsgebiete für die Arten basierend auf den ausschließlich hydrologischen Prädiktoren, deuten auf eine bessere Beschreibung des Abflussverhaltens durch die Prädiktoren hin. In dieser Arbeit habe ich hydrologische Variablen für SDMs erstellt und implementiert, effektive Methoden zur Verbesserung der Vorhersagen der Verbreitung von Fließgewässerarten entwickelt und validiert und somit die Forschung im Bereich der SDMs vorangebracht. Die entwickelten Methoden können sowohl in unterschiedlichen geographischen Regionen als auch für verschiedene Zeitschnitte und räumliche Skalen angewendet werden. Durch die Verbesserung der Modellgenauigkeit und der vorhergesagten Verbreitung kann die Anwendung von SDMs somit dazu beitragen Managemententscheidungen und Naturschutzbestrebungen zu unterstützen

Recovery and desistance : what the emerging recovery movement in the alcohol and drug area can learn from models of desistance from offending

In the last twenty years, the recovery movement in alcohol and other drugs (AOD) has emerged as a major influence on alcohol and drug policy and practice in the UK, US and Australia. In roughly the same period of time, the desistance movement has become increasingly prominent in academic criminology, and is increasingly influential in criminal justice practice, particularly in the area of probation. Furthermore, the populations involved in recovery and desistance research have significant overlap, yet there has been little shared learning across these areas. The current article explores the evolution of thinking around desistance and what lessons it might offer conceptual models of recovery. It will be argued that one of the most important shared assumptions relates to identity change, and the extent to which these identity changes are intrinsically social or 'relational'. The paper will advance a social identity model as a mechanism for understanding the journey to recovery or desistance and the centrality of reintegration into communities for a coherent model and public policy around addiction recovery

Depression and Posttraumatic Stress Symptoms After Perinatal Loss in a Population-Based Sample

Introduction: Perinatal loss is often a traumatic outcome for families. While there are limited data about depressive outcomes in small populations, information about depression and posttraumatic stress disorder among large racially and economically diverse populations is sparse. Methods: We collaborated with the Michigan Department of Community Health to conduct a longitudinal survey of bereaved mothers with stillbirth or infant death under 28 days of life and live-birth (control) mothers in Michigan. The study assessed 9-month mental health outcomes including self-reported symptoms of depression and posttraumatic stress disorder along with information about demographics, pregnancy and loss experience, social support, and past and present mental health and treatment. Results: Of 1400 women contacted by the State of Michigan, 609 completed surveys and were eligible to participate for a 44% response rate (377 bereaved mothers and 232 control mothers with live births). In multivariable analysis, bereaved women had nearly 4-fold higher odds of having a positive screen for depression and 7-fold higher odds of a positive screen for post-traumatic stress disorder after controlling for demographic and personal risk variables. A minority of screen-positive women were receiving any type of psychiatric treatment. Conclusion: This is the largest epidemiologically based study to date to measure the psychological impact of perinatal loss. Nine months after a loss, bereaved women showed high levels of distress with limited rates of treatment. Symptoms need to be monitored over time for persisting disorder and further research should identify women at highest risk for poor outcomes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140137/1/jwh.2015.5284.pd

Are depression and anxiety associated with disease activity in rheumatoid arthritis?:A prospective study

BACKGROUND: This study aimed to investigate the impact of depression and anxiety scores on disease activity at 1-year follow-up in people with Rheumatoid Arthritis (RA). METHODS: The Hospital Anxiety Depression Scale (HADS) was used to measure depression and anxiety in a cross-section of RA patients. The primary outcome of interest was disease activity (DAS28), measured one-year after baseline assessment. Secondary outcomes were: tender joint count, swollen joint count, erythrocyte sedimentation rate and patient global assessment, also measured one-year after baseline assessment. We also examined the impact of baseline depression and anxiety on odds of reaching clinical remission at 1-year follow-up. RESULTS: In total, 56 RA patients were eligible for inclusion in this analysis. Before adjusting for key demographic and disease variables, increased baseline depression and anxiety were associated with increased disease activity at one-year follow-up, although this was not sustained after adjusting for baseline disease activity. There was a strong association between depression and anxiety and the subjective components of the DAS28 at 12-month follow-up: tender joint count and patient global assessment. After adjusting for age, gender, disease duration and baseline tender joint count and patient global assessment respectively, higher levels of depression and anxiety at baseline were associated with increased tender joint count and patient global assessment scores at 1-year follow-up. CONCLUSIONS: Symptoms of depression and anxiety have implications for disease activity, as measured via the DAS28, primarily due to their influence on tender joints and patient global assessment. These findings have implications for treatment decision-making as inflated DAS28 despite well controlled inflammatory disease markers may indicate significant psychological morbidity and related non-inflammatory pain, rather than true disease activity

The UK Life in Recovery Survey 2015 : the first national UK survey of addiction recovery experiences

This report contains some of the first insights into how recovery has transformed the lives of many people in the UK. It is hoped that documenting the pathways to recovery and the benefits that recovery can infer on individuals, families and communities contained in this report can be used to inform policy makers about what promotes and enables recovery, and the pathways and timings of key recovery milestones. The key messages from the UK Life in Recovery survey 2015 are that recovery is attainable, is sustainable and is beneficial to a range of individuals and groups. Finally, that advancing our knowledge of recovery will reduce the stigma and discrimination that many in active addiction and recovery experience

Assessing Barriers to Community Pediatric Dental Needs

Introduction: Oral health is an often overlooked aspect of healthcare with many effects on an individual’s well-being. Dental caries is the most common chronic disease in children, and most dental problems are preventable. Barriers to accessing dental care for low income children include: oral health beliefs of parents, transportation issues, and difficulty locating providers who accept Medicaid. Investigation of the pediatrician’s role showed an increase in dental visits among children who were recommended for care by their primary care providers. Recent data indicates that 67.1% of Vermont Medicaid enrolled children received dental care within one calendar year. While indicating a gap in services, this is the highest rate in the U.S. A comprehensive national survey found that 85% of Vermont children received preventive care in the past year, while recent state data shows that 18% of Vermont children on Medicaid and 16% of children overall have untreated dental decay. In 2009, The Ronald McDonald House Charities, along with the Health Center of Plainfield, implemented the Vermont Ronald McDonald Care Mobile (RMCM), a traveling dental clinic providing dental care for Vermont’s underserved children. In one year, the RMCM visited 15 Vermont schools and treated 214 children, only 9% of the 2400 children projected. The RMCM currently serves sites in three Counties: Grand Isle, Orange, and Lamoille. The objective of our study was to investigate barriers to access to Dental care among Vermont children, with particular regard to the RMCM. The underutilization of the RMCM was assessed by researching current data on Vermont oral health and by surveying overall attitudes toward both the RMCM and Towns the RMCM visited in the past year pediatric dental care in Vermont.https://scholarworks.uvm.edu/comphp_gallery/1060/thumbnail.jp

Bounds on the Expected Size of the Maximum Agreement Subtree

We prove polynomial upper and lower bounds on the expected size of the maximum agreement subtree of two random binary phylogenetic trees under both the uniform distribution and Yule-Harding distribution. This positively answers a question posed in earlier work. Determining tight upper and lower bounds remains an open problem.Comment: Revised versio

Right Turn Veteran-Specific Recovery Service: 5 site evaluation pilot: Interim report

The Right Turn project works with the ex-service personnel community in recovery from substance misuse. This report presents the interim findings from a two-year evaluation on the impact on health and wellbeing outcomes on military veterans engaging in this innovative peer-focussed recovery service. The evaluation is designed around a structured quantitative data collection process using an established repeat measure design and utilises qualitative methodologies to explore both the life experiences of this veteran cohort and to take account of their own perceptions of the model of services they feel they require. This report suggests that the military veteran community experience distinct barriers to accessing main stream health and wellbeing services. Alongside comorbidity issues, management of chronic physical conditions and social isolation, this report demonstrates that this cohort's own previous military conditioning forms a further barrier to accessing support services. This report contains recommendations to inform generic support staff when encountering veterans within health and wellbeing settings

Right Turn Veteran-Specific Recovery Service: 5 site evaluation pilot : Interim report

The Right Turn project works with the ex-service personnel community in recovery from substance misuse. This report presents the interim findings from a two-year evaluation on the impact on health and wellbeing outcomes on military veterans engaging in this innovative peer-focussed recovery service. The evaluation is designed around a structured quantitative data collection process using an established repeat measure design and utilises qualitative methodologies to explore both the life experiences of this veteran cohort and to take account of their own perceptions of the model of services they feel they require. This report suggests that the military veteran community experience distinct barriers to accessing main stream health and wellbeing services. Alongside comorbidity issues, management of chronic physical conditions and social isolation, this report demonstrates that this cohort's own previous military conditioning forms a further barrier to accessing support services. This report contains recommendations to inform generic support staff when encountering veterans within health and wellbeing settings