37 research outputs found

    The State of Capacity Development Evaluation in Biodiversity Conservation and Natural Resource Management

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    Capacity development is critical to long-term conservation success, yet we lack a robust and rigorous understanding of how well its effects are being evaluated. A comprehensive summary of who is monitoring and evaluating capacity development interventions, what is being evaluated and how, would help in the development of evidence-based guidance to inform design and implementation decisions for future capacity development interventions and evaluations of their effectiveness. We built an evidence map by reviewing peer-reviewed and grey literature published since 2000, to identify case studies evaluating capacity development interventions in biodiversity conservation and natural resource management. We used inductive and deductive approaches to develop a coding strategy for studies that met our criteria, extracting data on the type of capacity development intervention, evaluation methods, data and analysis types, categories of outputs and outcomes assessed, and whether the study had a clear causal model and/or used a systems approach. We found that almost all studies assessed multiple outcome types: most frequent was change in knowledge, followed by behaviour, then attitude. Few studies evaluated conservation outcomes. Less than half included an explicit causal model linking interventions to expected outcomes. Half of the studies considered external factors that could influence the efficacy of the capacity development intervention, and few used an explicit systems approach. We used framework synthesis to situate our evidence map within the broader literature on capacity development evaluation. Our evidence map (including a visual heat map) highlights areas of low and high representation in investment in research on the evaluation of capacity development

    Reconciling conflicting evidence for the cause of the observed early 21st century Eurasian cooling

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    It is now well established that the Arctic is warming at a faster rate than the global average. This warming, which has been accompanied by a dramatic decline in sea ice, has been linked to cooling over the Eurasian subcontinent over recent decades, most dramatically during the period 1998–2012. This is a counter-intuitive impact under global warming given that land regions should warm more than ocean (and the global average). Some studies have proposed a causal teleconnection from Arctic sea-ice retreat to Eurasian wintertime cooling; other studies argue that Eurasian cooling is mainly driven by internal variability. Overall, there is an impression of strong disagreement between those holding the “ice-driven” versus “internal variability” viewpoints. Here, we offer an alternative framing showing that the sea ice and internal variability views can be compatible. Key to this is viewing Eurasian cooling through the lens of dynamics (linked primarily to internal variability with some potential contribution from sea ice; cools Eurasia) and thermodynamics (linked to sea-ice retreat; warms Eurasia). This approach, combined with recognition that there is uncertainty in the hypothesized mechanisms themselves, allows both viewpoints (and others) to co-exist and contribute to our understanding of Eurasian cooling. A simple autoregressive model shows that Eurasian cooling of this magnitude is consistent with internal variability, with some periods exhibiting stronger cooling than others, either by chance or by forced changes. Rather than posit a “yes-or-no” causal relationship between sea ice and Eurasian cooling, a more constructive way forward is to consider whether the cooling trend was more likely given the observed sea-ice loss, as well as other sources of low-frequency variability. Taken in this way both sea ice and internal variability are factors that affect the likelihood of strong regional cooling in the presence of ongoing global warming.</p

    Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

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    The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lowerstratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system¿s climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere.This work uses S2S Project data. S2S is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). This work was initiated by the Stratospheric Network for the Assessment of Predictability (SNAP), a joint activity of SPARC (WCRP) and the S2S Project (WWRP–WCRP). The work of Rachel W.-Y. Wu is funded through ETH grant ETH-05 19-1. Support from the Swiss National Science Foundation through projects PP00P2_170523 and PP00P2_198896 to Daniela I. V. Domeisen is gratefully acknowledged. Chaim I. Garfinkel and Chen Schwartz are supported by the ISF–NSFC joint research program (grant no. 3259/19). The work of Marisol Osman was supported by UBACyT20020170100428BA and PICT-2018-03046 projects. The work of Alvaro de la Cámara is funded by the Spanish Ministry of Science and Innovation through project PID2019-109107GB-I00. Blanca Ayarzagüena and Natalia Calvo acknowledge the support of the Spanish Ministry of Science and Innovation through the JeDiS (RTI2018-096402-B-I00) project. Froila M. Palmeiro and Javier García-Serrano have been partially supported by the Spanish ATLANTE project (PID2019-110234RB-C21) and Ramón y Cajal program (RYC-2016-21181), respectively. Neil P. Hindley and Corwin J. Wright are supported by UK Natural Environment Research Council (NERC), grant number NE/S00985X/1. Corwin J. Wright is also supported by a Royal Society University Research Fellowship UF160545. Seok-Woo Son and Hera Kim are supported by the Basic Science Research Program through the National Research Foundation of Korea (2017R1E1A1A01074889). This material is based upon work supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling program under award no. DE-SC0022070 and National Science Foundation (NSF) IA 1947282. This work was also supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. Pu Lin is supported by award NA18OAR4320123 from the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce. Zachary D. Lawrence was partially supported under NOAA award NA20NWS4680051; Zachary D. Lawrence and Judith Perlwitz also acknowledge support from US federally appropriated funds

    Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

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    The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lower-stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system\u27s climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere

    Real-world assessment of intravitreal dexamethasone implant (0.7 mg) in patients with macular edema: The CHROME study

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    © 2015 Lam et al.Background: The purpose of this study was to evaluate the real-world use, efficacy, and safety of one or more dexamethasone intravitreal implant(s) 0.7 mg (DEX implant) in patients with macular edema (ME). Methods: This was a retrospective cohort study of patients with ME secondary to retinal disease treated at ten Canadian retina practices, including one uveitis center. Best-corrected visual acuity (BCVA), central retinal thickness (CRT), intraocular pressure (IOP), glaucoma and cataract surgery, and safety data were collected from the medical charts of patients with ≥3 months of follow-up after the initial DEX implant.Results: One hundred and one patient charts yielded data on 120 study eyes, including diagnoses of diabetic ME (DME) (n=34), retinal vein occlusion (RVO, n=30; branch in 19 and central in 11), and uveitis (n=23). Patients had a mean age of 60.9 years, and 73.3% of the study eyes had ME for a duration of ≥12 months prior to DEX implant injection(s). Baseline mean (± standard error) BCVA was 0.63±0.03 logMAR (20/86 Snellen equivalents) and mean CRT was 474.4±18.2 μm. The mean number of DEX implant injections was 1.7±0.1 in all study eyes; 44.2% of eyes had repeat DEX implant injections (reinjection interval 2.3–4.9 months). The greatest mean peak changes in BCVA lines of vision occurred in study eyes with uveitis (3.3±0.6, P0.05). Significant decreases in CRT were observed: -255.6±43.6 µm for uveitis, -190.9±23.5 µm for DME, and -160.7±39.6 µm for RVO (P<0.0001 for all cohorts). IOP increases of ≥10 mmHg occurred in 20.6%, 24.1%, and 22.7% of DME, RVO, and uveitis study eyes, respectively. IOP-lowering medication was initiated in 29.4%, 16.7%, and 8.7% of DME, RVO, and uveitis study eyes, respectively. Glaucoma surgery was performed in 1.7% of all study eyes and cataract surgery in 29.8% of all phakic study eyes receiving DEX implant(s). onclusion: DEX implant(s) alone or combined with other treatments and/or procedures resulted in functional and anatomic improvements in long-standing ME associated with retinal disease.Link_to_subscribed_fulltex

    Exploring the relationship between plural values of nature, human well-being, and conservation and development intervention: Why it matters and how to do it?

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    1. Globally, land and seascapes across the bioculturally diverse tropics are in transition. Impacted by the demands of distant consumers, the processes of global environmental change and numerous interventions seeking climate, conservation and development goals, these transitions have the potential to impact the relationships and plurality of values held between people and place. 2. This paper is a Synthesis of seven empirical studies within the Special Feature (SF): ‘What is lost in transition? Capturing the impacts of conservation and development interventions on relational values and human wellbeing in the tropics’. Through two Open Forum workshops, and critical review, contributing authors explored emergent properties across the papers of the SF. Six core themes were identified and are subsumed within broad categories of: (i) the problem of reconciling scale and complexity, (ii) key challenges to be overcome for more plural understanding of social dimensions of landscape change and (iii) ways forward: the potential of an environmental justice framework, and a practical overview of methods available to do so. 3. The Synthesis interprets disparate fields and complex academic work on relational values, human well-being and de-colonial approaches in impact appraisal. It offers a practical and actionable catalogue of methods for plural valuation in the field, and reflects on their combinations, strengths and weaknesses. 4. The research contribution is policy relevant because it builds the case for why a more plural approach in intervention design and evaluation is essential for achieving more just and sustainable futures, and highlights some of the key actions points deemed necessary to achieve such a transition to conventional practice
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