Dataset Information

Integrating models of human behaviour between the individual and population levels to inform conservation interventions.

ABSTRACT: Conservation takes place within social-ecological systems, and many conservation interventions aim to influence human behaviour in order to push these systems towards sustainability. Predictive models of human behaviour are potentially powerful tools to support these interventions. This is particularly true if the models can link the attributes and behaviour of individuals with the dynamics of the social and environmental systems within which they operate. Here we explore this potential by showing how combining two modelling approaches (social network analysis, SNA, and agent-based modelling, ABM) could lead to more robust insights into a particular type of conservation intervention. We use our simple model, which simulates knowledge of ranger patrols through a hunting community and is based on empirical data from a Cambodian protected area, to highlight the complex, context-dependent nature of outcomes of information-sharing interventions, depending both on the configuration of the network and the attributes of the agents. We conclude by reflecting that both SNA and ABM, and many other modelling tools, are still too compartmentalized in application, either in ecology or social science, despite the strong methodological and conceptual parallels between their uses in different disciplines. Even a greater sharing of methods between disciplines is insufficient, however; given the impact of conservation on both the social and ecological aspects of systems (and vice versa), a fully integrated approach is needed, combining both the modelling approaches and the disciplinary insights of ecology and social science. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

SUBMITTER: Dobson ADM

PROVIDER: S-EPMC6710576 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrating models of human behaviour between the individual and population levels to inform conservation interventions.

Dobson Andrew D M ADM de Lange Emiel E Keane Aidan A Ibbett Harriet H Milner-Gulland E J EJ

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20190729 1781

Conservation takes place within social-ecological systems, and many conservation interventions aim to influence human behaviour in order to push these systems towards sustainability. Predictive models of human behaviour are potentially powerful tools to support these interventions. This is particularly true if the models can link the attributes and behaviour of individuals with the dynamics of the social and environmental systems within which they operate. Here we explore this potential by showi ...[more]

PMID: 31352880

Similar Datasets

Project description:BackgroundImproving hand hygiene (HH) compliance is one of the most important, but elusive, goals of infection control. The purpose of this study was to use the capability (C), opportunity (O), motivation (M), and behaviour (B; COM-B) model and the theoretical domains framework (TDF) to gain an understanding of the barriers and enablers of HH behaviours in an intensive care unit (ICU) in order to identify specific interventions to improve HH compliance.MethodsA semi-structured interview schedule was developed based upon the COM-B model. This schedule was used to interview a total of 26 ICU staff: 12 ICU nurses, 11 anaesthetic specialist registrars, and three anaesthetic senior house officers.ResultsParticipants were confident in their capabilities to carry out appropriate HH behaviours. The vast majority of participants reported having the necessary knowledge and skills, and believed they were capable of carrying out appropriate HH behaviours. Social influence was regarded as being important in encouraging HH compliance by the interviewees- particularly by nurses. The participants were motivated to carry out HH behaviours, and it was recognised that HH was an important part of their job and is important in preventing infection. It is recommended that staff are provided with targeted HH training, in which individuals receive direct and individualised feedback on actual performance and are provided guidance on how to address deficiencies in HH compliance at the bedside at the time at which the HH behaviour is performed. Modelling of appropriate HH behaviours by senior leaders is also suggested, particularly by senior doctors. Finally, appropriate levels of staffing are a factor that must be considered if HH compliance is to be improved.ConclusionsThis study has demonstrated that short interviews with ICU staff, founded on appropriate behavioural change frameworks, can provide an understanding of HH behaviour. This understanding can then be applied to design interventions appropriately tailored to the needs of a specific unit, which will have an increased likelihood of improving HH compliance.

Project description:BackgroundHaplotype information is useful for many genetic analyses and haplotypes are usually inferred using computational approaches. Among such approaches, the importance of single individual haplotyping (SIH), which infers individual haplotypes from sequence fragments, has been increasing with the advent of novel sequencing techniques, such as dilution-based sequencing. These techniques could produce virtual long read fragments by separating DNA fragments into multiple low-concentration aliquots, sequencing and mapping each aliquot, and merging clustered short reads. Although these experimental techniques are sophisticated, they have the problem of producing chimeric fragments whose left and right parts match different chromosomes. In our previous research, we found that chimeric fragments significantly decrease the accuracy of SIH. Although chimeric fragments can be removed by using haplotypes which are determined from pedigree genotypes, pedigree genotypes are generally not available. The length of reads cluster and heterozygous calls were also used to detect chimeric fragments. Although some chimeric fragments will be removed with these features, considerable number of chimeric fragments will be undetected because of the dispersion of the length and the absence of SNPs in the overlapped regions. For these reasons, a general method to detect and remove chimeric fragments is needed.ResultsIn this paper, we propose a general method to detect chimeric fragments. The basis of our method is that a chimeric fragment would correspond to an artificial recombinant haplotype and would differ from biological haplotypes. To detect differences from biological haplotypes, we integrated statistical phasing, which is a haplotype inference approach from population genotypes, into our method. We applied our method to two datasets and detected chimeric fragments with high AUC. AUC values of our method are higher than those of just using cluster length and heterozygous calls. We then used multiple SIH algorithm to compare the accuracy of SIH before and after removing the chimeric fragment candidates. The accuracy of assembled haplotypes increased significantly after removing chimeric fragment candidates.ConclusionsOur method is useful for detecting chimeric fragments and improving SIH accuracy. The Ruby script is available at https://sites.google.com/site/hmatsu1226/software/csp.

Project description:BackgroundSince 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach.MethodsThis study used individual and household-level data from the 2015 and 2018 annual malaria indicator surveys on Bioko Island, as well as remotely-sensed environmental data in multilevel logistic regression models to quantify the odds of malaria infection. The analyses were stratified by urban and rural settings and by survey year.ResultsMalaria prevalence was higher in 10-14-year-old children and similar between female and male individuals. After adjusting for demographic factors and other covariates, many of the variables investigated showed no significant association with malaria infection. The factor most strongly associated was history of travel to mainland Equatorial Guinea (mEG), which increased the odds significantly both in urban and rural settings (people who travelled had 4 times the odds of infection). Sleeping under a long-lasting insecticidal net decreased significantly the odds of malaria across urban and rural settings and survey years (net users had around 30% less odds of infection), highlighting their contribution to malaria control on the Island. Improved housing conditions indicated some protection, though this was not consistent across settings and survey year.ConclusionsMalaria risk on Bioko Island is heterogeneous and determined by a combination of factors interacting with local mosquito ecology. These interactions grant further investigation in order to better adapt control according to need. The single most important risk factor identified was travel to mEG, in line with previous investigations, and represents a great challenge for the success of malaria control on the Island.

Dataset Information

Integrating models of human behaviour between the individual and population levels to inform conservation interventions.

Publications

Integrating models of human behaviour between the individual and population levels to inform conservation interventions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets