The capacity of refugia for conservation planning under climate change

Refugia – areas that may facilitate the persistence of species during large-scale, long-term climatic change – are increasingly important for conservation planning. There are many methods for identifying refugia, but the ability to quantify their potential for facilitating species persistence (ie their “capacity”) remains elusive. We propose a flexible framework for prioritizing future refugia, based on their capacity. This framework can be applied through various modeling approaches and consists of three steps: (1) definition of scope, scale, and resolution; (2) identification and quantification; and (3) prioritization for conservation. Capacity is quantified by multiple indicators, including environmental stability, microclimatic heterogeneity, size, and accessibility of the refugium. Using an integrated, semi-mechanistic modeling technique, we illustrate how this approach can be implemented to identify refugia for the plant diversity of Tasmania, Australia. The highest- capacity climate-change refugia were found primarily in cool, wet, and topographically complex environments, several of which we identify as high priorities for biodiversity conservation and management

Examining differential responses to the Take Care of Me trial: A latent class and moderation analysis

Given prevalent alcohol misuse-emotional comorbidities among young adults, we developed an internet-based integrated treatment called Take Care of Me. Although the treatment had an impact on several secondary outcomes, effects were not observed for the primary outcome. Therefore, the goal of the current study was to examine heterogeneity in treatment responses. The initial RCT randomized participants to either a treatment or psychoeducational control condition. We conducted an exploratory latent class analysis to distinguish individuals based on pre-treatment risk and then used moderated regressions to examine differential treatment responses based on class membership. We found evidence for three distinct groups. Most participants fell in the “low severity” group (n = 123), followed by the “moderate severity” group (n = 57) who had a higher likelihood of endorsing a previous mental health diagnosis and treatment and higher symptom severity than the low group. The “high severity” group (n = 42) endorsed a family history of alcoholism, and the highest symptom severity and executive dysfunction. Moderated regressions revealed significant class differences in treatment responses. In the treatment condition, high severity (relative to low) participants reported higher alcohol consumption and hazardous drinking and lower quality of life at follow-up, whereas moderate severity (relative to low) individuals had lower alcohol consumption at follow-up, and lower hazardous drinking at end-of-treatment. No class differences were found for participants in the control group. Higher risk individuals in the treatment condition had poorer responses to the program. Tailoring interventions to severity may be important to examine in future research

Cross-scale analysis of social-ecological systems:Policy options appraisal for delivering NetZero and other environmental objectives in Scotland

Public policy confronts complex, contested, wicked problems such as climate and biodiversity crises with challenges of how issues are framed, analysed, codified, and interpreted. Social-ecological systems provide an analytical framework that couples the biosphere and technosphere, recognising biophysical limits and emphasising the importance of critical reflection within policy decision-making. Conducting policy-options appraisals is increasingly seen as a transdisciplinary research-policy endeavour with researchers engaging policy actors in an extended peer community (post-normal science). This paper presents a case study of analysis undertaken with researchers, policy analysts, policy makers and other stakeholders to support decisions on how to implement future agriculture support in Scotland, so that the policy programme better delivers across social, economic and environmental objectives. The key change being considered in the future agricultural support programme is Enhanced Conditionality (EC) where the level of financial support provided to farm-businesses will depend on their undertaking agri-environmental measures that deliver against the key priorities of reducing greenhouse gas emissions and reversing biodiversity losses. The paper outlines the policy context within which the EC options appraisal takes place – highlighting how EC is a crucial component in making the wider suite of policy measures work. The transdisciplinary approach, Quantitative Story Telling (QST) is presented, emerging from decision support, participatory research, and post-normal science for policy domains. The stages of QST highlight the importance of analysis that underpins any quantification (decision on how issues are framed and what it included in the analysis) and the expectation that research outputs with be deliberated on with, and interpreted from, stakeholder perspectives. The project specific analyses are outlined, combining top-down options appraisal of how macro-policy decisions could constrain EC and bottom-up analysis of potential uptake and effectiveness of EC measures, undertaken in inter-disciplinary workshops with domain experts from biodiversity, soils and waters. The paper highlights challenges for implementation and evaluation at meso-scale with interactions between farm-businesses and catchment, landscape and regional objectives. The conclusions of the analysis, in policy terms, are that EC presents an opportunity to significantly realign how agricultural land management is conducted in Scotland, so that it is more effective in delivering climate change and biodiversity objectives, but there are formidable challenges in resolving the policy “sudoku”. Meso-scale issues are likely to mean the need to integrate alternative modelling paradigms such as spatial, empirical agent-based modelling (ABM) into policy option appraisals. By taking multi-scale, social-ecological systems perspectives on EC it has been possible to identify key policy decisions at a range of scales on which the success of EC will depend, to have a realistic understanding of how effective the EC measures might be in heterogenous Scottish environments and what are the likely barriers to uptake. The analysis also highlighted where outcomes of the policy change are likely to be challenging to monitor-evaluate; and where there are dependencies between farm-businesses that mean EC measures need to be supplemented with mechanisms that (1) promote cooperation between land managers and (2) identify and respond to agreed local priorities. The value of the participatory QST process was in making sure the analyses being undertaken were salient and the outputs seen as credible – but the challenges of interpreting necessarily complex outputs remain. The greatest value of QST may be that it provides a structured way to navigate complexity with policy makers rather than seeking to control or eliminate it.</p

Cross-scale analysis of social-ecological systems:Policy options appraisal for delivering NetZero and other environmental objectives in Scotland

Public policy confronts complex, contested, wicked problems such as climate and biodiversity crises with challenges of how issues are framed, analysed, codified, and interpreted. Social-ecological systems provide an analytical framework that couples the biosphere and technosphere, recognising biophysical limits and emphasising the importance of critical reflection within policy decision-making. Conducting policy-options appraisals is increasingly seen as a transdisciplinary research-policy endeavour with researchers engaging policy actors in an extended peer community (post-normal science). This paper presents a case study of analysis undertaken with researchers, policy analysts, policy makers and other stakeholders to support decisions on how to implement future agriculture support in Scotland, so that the policy programme better delivers across social, economic and environmental objectives. The key change being considered in the future agricultural support programme is Enhanced Conditionality (EC) where the level of financial support provided to farm-businesses will depend on their undertaking agri-environmental measures that deliver against the key priorities of reducing greenhouse gas emissions and reversing biodiversity losses. The paper outlines the policy context within which the EC options appraisal takes place – highlighting how EC is a crucial component in making the wider suite of policy measures work. The transdisciplinary approach, Quantitative Story Telling (QST) is presented, emerging from decision support, participatory research, and post-normal science for policy domains. The stages of QST highlight the importance of analysis that underpins any quantification (decision on how issues are framed and what it included in the analysis) and the expectation that research outputs with be deliberated on with, and interpreted from, stakeholder perspectives. The project specific analyses are outlined, combining top-down options appraisal of how macro-policy decisions could constrain EC and bottom-up analysis of potential uptake and effectiveness of EC measures, undertaken in inter-disciplinary workshops with domain experts from biodiversity, soils and waters. The paper highlights challenges for implementation and evaluation at meso-scale with interactions between farm-businesses and catchment, landscape and regional objectives. The conclusions of the analysis, in policy terms, are that EC presents an opportunity to significantly realign how agricultural land management is conducted in Scotland, so that it is more effective in delivering climate change and biodiversity objectives, but there are formidable challenges in resolving the policy “sudoku”. Meso-scale issues are likely to mean the need to integrate alternative modelling paradigms such as spatial, empirical agent-based modelling (ABM) into policy option appraisals. By taking multi-scale, social-ecological systems perspectives on EC it has been possible to identify key policy decisions at a range of scales on which the success of EC will depend, to have a realistic understanding of how effective the EC measures might be in heterogenous Scottish environments and what are the likely barriers to uptake. The analysis also highlighted where outcomes of the policy change are likely to be challenging to monitor-evaluate; and where there are dependencies between farm-businesses that mean EC measures need to be supplemented with mechanisms that (1) promote cooperation between land managers and (2) identify and respond to agreed local priorities. The value of the participatory QST process was in making sure the analyses being undertaken were salient and the outputs seen as credible – but the challenges of interpreting necessarily complex outputs remain. The greatest value of QST may be that it provides a structured way to navigate complexity with policy makers rather than seeking to control or eliminate it.</p

Error-analysis and comparison to analytical models of numerical waveforms produced by the NRAR Collaboration

The Numerical-Relativity-Analytical-Relativity (NRAR) collaboration is a joint effort between members of the numerical relativity, analytical relativity and gravitational-wave data analysis communities. The goal of the NRAR collaboration is to produce numerical-relativity simulations of compact binaries and use them to develop accurate analytical templates for the LIGO/Virgo Collaboration to use in detecting gravitational-wave signals and extracting astrophysical information from them. We describe the results of the first stage of the NRAR project, which focused on producing an initial set of numerical waveforms from binary black holes with moderate mass ratios and spins, as well as one non-spinning binary configuration which has a mass ratio of 10. All of the numerical waveforms are analysed in a uniform and consistent manner, with numerical errors evaluated using an analysis code created by members of the NRAR collaboration. We compare previously-calibrated, non-precessing analytical waveforms, notably the effective-one-body (EOB) and phenomenological template families, to the newly-produced numerical waveforms. We find that when the binary's total mass is ~100-200 solar masses, current EOB and phenomenological models of spinning, non-precessing binary waveforms have overlaps above 99% (for advanced LIGO) with all of the non-precessing-binary numerical waveforms with mass ratios <= 4, when maximizing over binary parameters. This implies that the loss of event rate due to modelling error is below 3%. Moreover, the non-spinning EOB waveforms previously calibrated to five non-spinning waveforms with mass ratio smaller than 6 have overlaps above 99.7% with the numerical waveform with a mass ratio of 10, without even maximizing on the binary parameters.Comment: 51 pages, 10 figures; published versio

Resistance to autosomal dominant Alzheimer's disease in an APOE3 Christchurch homozygote: a case report.

We identified a PSEN1 (presenilin 1) mutation carrier from the world's largest autosomal dominant Alzheimer's disease kindred, who did not develop mild cognitive impairment until her seventies, three decades after the expected age of clinical onset. The individual had two copies of the APOE3 Christchurch (R136S) mutation, unusually high brain amyloid levels and limited tau and neurodegenerative measurements. Our findings have implications for the role of APOE in the pathogenesis, treatment and prevention of Alzheimer's disease

Priority questions for biodiversity conservation in the Mediterranean biome: Heterogeneous perspectives across continents and stakeholders

The identification of research questions with high relevance for biodiversity conservation is an important step towards designing more effective policies and management actions, and to better allocate funding among alternative conservation options. However, the identification of priority questions may be influenced by regional differences in biodiversity threats and social contexts, and to variations in the perceptions and interests of different stakeholders. Here we describe the results of a prioritization exercise involving six types of stakeholders from the Mediterranean biome, which includes several biodiversity hotspots spread across five regions of the planet (Europe, Africa, North and South America, and Australia). We found great heterogeneity across regions and stakeholder types in the priority topics identified and disagreement among the priorities of research scientists and other stakeholders. However, governance, climate change, and public participation issues were key topics in most regions. We conclude that the identification of research priorities should be targeted in a way that integrates the spectrum of stakeholder interests, potential funding sources and regional needs, and that further development of interdisciplinary studies is required. The key questions identified here provide a basis to identify priorities for research funding aligned with biodiversity conservation needs in this biome

Rapid characterisation of vegetation structure to predict refugia and climate change impacts across a global biodiversity hotspot

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region.Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R2 of 0.8–0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia

The spectrum and clinical impact of epigenetic modifier mutations in myeloma

Epigenetic dysregulation is known to be an important contributor to myeloma pathogenesis but, unlike in other B cell malignancies, the full spectrum of somatic mutations in epigenetic modifiers has not been previously reported. We sought to address this using results from whole-exome sequencing in the context of a large prospective clinical trial of newly diagnosed patients and targeted sequencing in a cohort of previously treated patients for comparison.Whole-exome sequencing analysis of 463 presenting myeloma cases entered in the UK NCRI Myeloma XI study and targeted sequencing analysis of 156 previously treated cases from the University of Arkansas for Medical Sciences. We correlated the presence of mutations with clinical outcome from diagnosis and compared the mutations found at diagnosis with later stages of disease.In diagnostic myeloma patient samples we identify significant mutations in genes encoding the histone 1 linker protein, previously identified in other B-cell malignancies. Our data suggest an adverse prognostic impact from the presence of lesions in genes encoding DNA methylation modifiers and the histone demethylase KDM6A/UTX. The frequency of mutations in epigenetic modifiers appears to increase following treatment most notably in genes encoding histone methyltransferases and DNA methylation modifiers.Numerous mutations identified raise the possibility of targeted treatment strategies for patients either at diagnosis or relapse supporting the use of sequencing-based diagnostics in myeloma to help guide therapy as more epigenetic targeted agents become available