Imperial launches interactive map to track air pollution levels in public spaces
Solar Orbiter spacecraft heads to launch site on its way to the Sun
Imperial renews partnership with Times newspapers
Please contact your Faculty Web Officer to add a Research publications feed to a page/section
We explore the large spatial variation in the relationship between population density and burned area, usingcontinental-scale Geographically Weighted Regression (GWR) based on 13 years of satellite-derived burned areamaps from the global fire emissions database (GFED) and the human population density from the gridded populationof the world (GPW 2005). Significant relationships are observed over 51.5% of the global land area, and the areaaffected varies from continent to continent: population density has a significant impact on fire over most of Asia andAfrica but is important in explaining fire over < 22% of Europe and Australia. Increasing population density isassociated with both increased and decreased in fire. The nature of the relationship depends on land-use: increasingpopulation density is associated with increased burned are in rangelands but with decreased burned area incroplands. Overall, the relationship between population density and burned area is non-monotonic: burned areainitially increases with population density and then decreases when population density exceeds a threshold. Thesethresholds vary regionally. Our study contributes to improved understanding of how human activities relate to burnedarea, and should contribute to a better estimate of atmospheric emissions from biomass burning.
A novel framework is presented for the analysis of ecophysiological field measurements and modelling. The hypothesis ‘leaves minimise the summed unit costs of transpiration and carboxylation’ predicts leaf‐internal/ambient CO2 ratios (ci/ca) and slopes of maximum carboxylation rate (Vcmax) or leaf nitrogen (Narea) vs. stomatal conductance. Analysis of data on woody species from contrasting climates (cold‐hot, dry‐wet) yielded steeper slopes and lower mean ci/ca ratios at the dry or cold sites than at the wet or hot sites. High atmospheric vapour pressure deficit implies low ci/ca in dry climates. High water viscosity (more costly transport) and low photorespiration (less costly photosynthesis) imply low ci/ca in cold climates. Observed site‐mean ci/ca shifts are predicted quantitatively for temperature contrasts (by photorespiration plus viscosity effects) and approximately for aridity contrasts. The theory explains the dependency of ci/ca ratios on temperature and vapour pressure deficit, and observed relationships of leaf δ13C and Narea to aridity.
The consensus is that both ecological and social factors are essential dimensions of conservation research and practice. However, much of the literature on multiple disciplinary collaboration focuses on the difficulties of undertaking it. This review of the challenges of conducting multiple disciplinary collaboration offers a framework for thinking about the diversity and complexity of this endeavor. We focused on conceptual challenges, of which 5 main categories emerged: methodological challenges, value judgments, theories of knowledge, disciplinary prejudices, and interdisciplinary communication. The major problems identified in these areas have proved remarkably persistent in the literature surveyed (c.1960–2012). Reasons for these failures to learn from past experience include the pressure to produce positive outcomes and gloss over disagreements, the ephemeral nature of many such projects and resulting lack of institutional memory, and the apparent complexity and incoherence of the endeavor. We suggest that multiple disciplinary collaboration requires conceptual integration among carefully selected multiple disciplinary team members united in investigating a shared problem or question. We outline a 9-point sequence of steps for setting up a successful multiple disciplinary project. This encompasses points on recruitment, involving stakeholders, developing research questions, negotiating power dynamics and hidden values and conceptual differences, explaining and choosing appropriate methods, developing a shared language, facilitating on-going communications, and discussing data integration and project outcomes. Although numerous solutions to the challenges of multiple disciplinary research have been proposed, lessons learned are often lost when projects end or experienced individuals move on. We urge multiple disciplinary teams to capture the challenges recognized, and solutions proposed, by their researchers while projects are in process. A database of we
Conservation scientists are increasingly focusing on the drivers of human behavior and on theimplications of various sources of uncertainty for management decision making. Trophy hunting has beensuggested as a conservation tool because it gives economic value to wildlife, but recent examples show thatoverharvesting is a substantial problem and that data limitations are rife. We use a case study of trophyhunting of an endangered antelope, the mountain nyala (Tragelaphus buxtoni), to explore how uncertaintiesgenerated by population monitoring and poaching interact with decision making by 2 key stakeholders: thesafari companies and the government. We built a management strategy evaluation model that encompassesthe population dynamics of mountain nyala, a monitoring model, and a company decision making model. Weinvestigated scenarios of investment into antipoaching and monitoring by governments and safari companies.Harvest strategy was robust to the uncertainty in the population estimates obtained from monitoring, butpoaching had a much stronger effect on quota and sustainability. Hence, reducing poaching is in the interestsof companies wishing to increase the profitability of their enterprises, for example by engaging communitymembers as game scouts. There is a threshold level of uncertainty in the population estimates beyond whichthe year-to-year variation in the trophy quota prevented planning by the safari companies. This suggests a rolefor government in ensuring that a baseline level of population monitoring is carried out such that this levelis not exceeded. Our results illustrate the importance of considering the incentives of multiple stakeholderswhen designing frameworks for resource use and when designing management frameworks to address theparticular sources of uncertainty that affect system sustainability most heavily.
Assessing anthropogenic effects on biological diversity, identifying drivers of human behavior, and motivating behavioral change are at the core of effective conservation. Yet knowledge of people's behaviors is often limited because the true extent of natural resource exploitation is difficult to ascertain, particularly if it is illegal. To obtain estimates of rule-breaking behavior, a technique has been developed with which to ask sensitive questions. We used this technique, unmatched-count technique (UCT), to provide estimates of bushmeat poaching, to determine motivation and seasonal and spatial distribution of poaching, and to characterize poaching households in the Serengeti. We also assessed the potential for survey biases on the basis of respondent perceptions of understanding, anonymity, and discomfort. Eighteen percent of households admitted to being involved in hunting. Illegal bushmeat hunting was more likely in households with seasonal or full-time employment, lower household size, and longer household residence in the village. The majority of respondents found the UCT questions easy to understand and were comfortable answering them. Our results suggest poaching remains widespread in the Serengeti and current alternative sources of income may not be sufficiently attractive to compete with the opportunities provided by hunting. We demonstrate that the UCT is well suited to investigating noncompliance in conservation because it reduces evasive responses, resulting in more accurate estimates, and is technically simple to apply. We suggest that the UCT could be more widely used, with the trade-off being the increased complexity of data analyses and requirement for large sample sizes.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.