Birds as indicators of change in the freshwater ecosystems of Botswana

Freshwater ecosystems support highly biodiverse plant and animal populations and provide crucial ecosystem services to human communities. Despite this importance, these systems are being degraded faster than terrestrial or marine environments, resulting in large global declines in freshwater biodiversity. To track such environmental change, birds are often used as indicator species. I focused on tracking changes in significant waterbird breeding colonies, rivers and internationally listed wetlands in Botswana facing a wide range of threats. I identified that riparian bird communities along the Chobe River were more biodiverse in sites with the presence of large herbivores, highlighting the direct and indirect relationships between these seemingly unconnected taxa. Using a drone, I explored the relationships between waterbird breeding and river levels and inundation. Drone imagery on the Chobe River provided comprehensive data on the reproductive success, size and composition of the Kasane waterbird breeding colony, which were linked to river levels and inundation, while citizen science collected abundance data helped identify a threshold river level to support large waterbird breeding colonies. This underlined the importance of river flows for waterbird populations and the potential for the breeding of waterbirds to inform river management. Similarly in the Okavango Delta, citizen science data highlighted positive relationships between waterbird abundance and river flows, but there were indications of long-term declines in waterbird abundances. River flows were again important for waterbird breeding, with key waterbird breeding colonies located in areas experiencing moderate to high flood frequencies. I also developed a semi-automated counting technique for investigating colony sizes with a drone, negating the need to physically enter a colony or manually count imagery, saving time in image processing and ensuring researcher safety. Finally, I investigated the potential effects of foraging at landfill on the marabou stork. Plastics formed a significant proportion of marabou regurgitant while trace metal concentrations in feathers were higher than in naturally foraging populations, indicating potential deleterious impacts. My work highlighted the value of riparian bird communities, predominantly waterbirds, as indicators of change, reflecting herbivore population structures, land use alterations and changes in freshwater flows and inundation

A framework for post-project evaluation of multicriteria decision aiding processes from the stakeholders’ perspective : design and application

Numerous multicriteria decision aiding (MCDA) methods have been developed over the last decades and are now applied in various domains, sometimes using facilitated group workshops to create models. These models are all designed to improve decision processes. However, the lack of follow-up and post-project evaluations limit the understanding of how the participants experienced the group workshops and how the results were subsequently used within the organization. This is in contrast with the public participation research field, where a rich literature was developed for a posteriori evaluation of projects. Based on this literature, our research proposes a framework to evaluate, ex-post, MCDA projects. In order to illustrate this framework, we apply it to an MCDA project in Quebec City where a spatial decision support system to prioritize the redesign of streets as Complete Streets was built. Individual interviews were conducted with the Quebec City professionals that currently use, were leaders of the project, or have participated in the development of the decision support system. This research has identified that the need for change of practices within the workplace, communication problems, and the requirement for multidisciplinary work were at the root of the various challenges encountered during the workshops. Based on our experience, we propose some lessons learned and potential solutions that can enhance the body of literature in MCDA

A review of cost–benefit analysis and multicriteria decision analysis from the perspective of sustainable transport in project evaluation

Transport decision processes have traditionally applied cost-benefit analysis (CBA) with benefits mainly relating to time savings, and costs relating to infrastructure and maintenance costs. However, a shift toward more sustainable practices was initiated over the last decades to remedy the many negative impacts of automobility. As a result, decision processes related to transport projects have become more complex due to the multidimensional aspects and to the variety of stakeholders involved, often with conflicting points of view. To support rigorous decision making, multicriteria decision analysis (MCDA) is, in addition to CBA, often used by governments and cities. However, there is still no consensus in the transport field regarding a preferred method that can integrate sustainability principles. This paper presents a descriptive literature review related to MCDA and CBA in the field of transport. Among the 66 considered papers, we identified the perceived strengths and weaknesses of CBA and MCDA, the different ways to combine them and the ability of each method to support sustainable transport decision processes. We further analysed the results based on four types of rationality (objectivist, conformist, adjustive and reflexive). Our results show that both methods can help improve the decision processes and that, depending on the rationality adopted, the perceived strengths and weaknesses of MCDA and CBA can vary. Nonetheless, we observe that by adopting a more global and holistic perspective and by facilitating the inclusion of a participative process, MCDA, or a combination of both methods, emerge as the more promising appraisal methods for sustainable transport

Rapid literature mapping on the recent use of machine learning for wildlife imagery

Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, and classify animals and their behaviours. Yet, we currently have very few systematic literature surveys on its use in wildlife imagery. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types. We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species. An increasing number of studies have been published in ecology-specific journals indicating the uptake of deep learning to transform the detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation. Our survey, augmented with bibliometric analyses, provides valuable signposts for future studies to resolve and address shortcomings, gaps, and biases

Detection of a novel, integrative aging process suggests complex physiological integration

Abstract: Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels ofmany biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women’s Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis “integrated albunemia.” Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty – but not chronic disease – even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organizationmore generally