Conservation value, biodiversity value and methods of assessment in regenerating and human disturbed tropical forest

Although the value of primary forests for biodiversity conservation is well known, the potential biodiversity and conservation value of regenerating forests remains controversial. Many factors likely contribute to this, including: 1. the variable ages of regenerating forests being studied (often dominated by relatively young regenerating forests); 2. the potential for confounding on-going human disturbance (such as logging and hunting); 3. the relatively low number of multi-taxa studies; 4. the lack of studies that directly compare different historic disturbances within the same location; 5. contrasting patterns from different survey methodologies and the paucity of knowledge on the impacts across different vertical levels of rainforest biodiversity (often due to a lack of suitable methodologies available to assess them). We also know relatively little as to how biodiversity is affected by major current impacts, such as unmarked rainforest roads, which contribute to this degradation of habitat and fragmentation. This thesis explores the potential biodiversity value of regenerating rainforests under the best of scenarios and seeks to understand more about the impact of current human disturbance to biodiversity; data comes from case studies from the Manu and Sumaco Biosphere Reserves in the Western Amazon. Specifically, I compare overall biodiversity and conservation value of a best case regenerating rainforest site with a selection of well-studied primary forest sites and with predicted species lists for the region; including a focus on species of key conservation concern. I then investigate the biodiversity of the same study site in reference to different types of historic anthropogenic disturbance. Following this I investigate the impacts to biodiversity from an unmarked rainforest road. In order to understand more about the differential effects of habitat disturbance on arboreal diversity I directly assess how patterns of butterfly biodiversity vary between three vertical strata. Although assessments within the canopy have been made for birds, invertebrates and bats, very few studies have successfully targeted arboreal mammals. I therefore investigate the potential of camera traps for inventorying arboreal mammal species in comparison with traditional methodologies. Finally, in order to investigate the possibility that different survey methodologies might identify different biodiversity patterns in habitat disturbance assessments, I investigate whether two different but commonly used survey methodologies used to assess amphibians, indicate the same or different responses of amphibian biodiversity to historic habitat change by people. The regenerating rainforest study site contained high levels of species richness; both in terms of alpha diversity found in nearby primary forest areas (87% ±3.5) and in terms of predicted primary forest diversity from the region (83% ±6.7). This included 89% (39 out of 44) of the species of high conservation concern predicted for the Manu region. Faunal species richness in once completely cleared regenerating forest was on average 13% (±9.8) lower than historically selectively logged forest. The presence of the small unmarked road significantly altered levels of faunal biodiversity for three taxa, up to and potentially beyond 350m into the forest interior. Most notably, the impact on biodiversity extended to at least 32% of the whole reserve area. The assessment of butterflies across strata showed that different vertical zones within the same rainforest responded differently in areas with different historic human disturbance. A comparison between forest regenerating after selective logging and forest regenerating after complete clearance, showed that there was a 17% greater reduction in canopy species richness in the historically cleared forest compared with the terrestrial community. Comparing arboreal camera traps with traditional ground-based techniques suggests that camera traps are an effective tool for inventorying secretive arboreal rainforest mammal communities and detect a higher number of cryptic species. Finally, the two survey methodologies used to assess amphibian communities identified contrasting biodiversity patterns in a human modified rainforest; one indicated biodiversity differences between forests with different human disturbance histories, whereas the other suggested no differences between forest disturbance types. Overall, in this thesis I find that the conservation and biodiversity value of regenerating and human disturbed tropical forest can potentially contribute to rainforest biodiversity conservation, particularly in the best of circumstances. I also highlight the importance of utilising appropriate study methodologies that to investigate these three-dimensional habitats, and contribute to the development of methodologies to do so. However, care should be taken when using different survey methodologies, which can provide contrasting biodiversity patterns in response to human disturbance.

Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms

Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.