A framework for collaborative wolverine connectivity conservation.
Ontology highlight
ABSTRACT:
SUBMITTER: Carroll KA
PROVIDER: S-EPMC8326200 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:I describe a prioritization protocol for future wolverine habitat connectivity conservation using integer linear programming. Conservation prioritization has broad applications across scales, systems, and species. However, the process of preparing, generating, and analyzing the necessary data can be complex. Thus, this protocol details the process from data acquisition to implementation. For complete details on the use and execution of this protocol, please refer to Carroll et al. (2020) and Carroll et al. (2021).
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Project description:Understanding a species' behavioral response to rapid environmental change is an ongoing challenge in modern conservation. Anthropogenic landscape modification, or "human footprint," is well documented as a central cause of large mammal decline and range contractions where the proximal mechanisms of decline are often contentious. Direct mortality is an obvious cause; alternatively, human-modified landscapes perceived as unsuitable by some species may contribute to shifts in space use through preferential habitat selection. A useful approach to tease these effects apart is to determine whether behaviors potentially associated with risk vary with human footprint. We hypothesized wolverine (Gulo gulo) behaviors vary with different degrees of human footprint. We quantified metrics of behavior, which we assumed to indicate risk perception, from photographic images from a large existing camera-trapping dataset collected to understand wolverine distribution in the Rocky Mountains of Alberta, Canada. We systematically deployed 164 camera sites across three study areas covering approximately 24,000 km(2), sampled monthly between December and April (2007-2013). Wolverine behavior varied markedly across the study areas. Variation in behavior decreased with increasing human footprint. Increasing human footprint may constrain potential variation in behavior, through either restricting behavioral plasticity or individual variation in areas of high human impact. We hypothesize that behavioral constraints may indicate an increase in perceived risk in human-modified landscapes. Although survival is obviously a key contributor to species population decline and range loss, behavior may also make a significant contribution.
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Project description:We report a chromosomal-level genome assembly of a male North American wolverine (Gulo gulo luscus) from the Kugluktuk region of Nunavut, Canada. The genome was assembled directly from long-reads, comprising: 758 contigs with a contig N50 of 36.6 Mb; contig L50 of 20; base count of 2.39 Gb; and a near complete representation (99.98%) of the BUSCO 5.2.2 set of 9,226 genes. A presumptive chromosomal-level assembly was generated by scaffolding against two chromosomal-level Mustelidae reference genomes, the ermine and the Eurasian river otter, to derive a final scaffold N50 of 144.0 Mb and a scaffold L50 of 7. We annotated a comprehensive set of genes that have been associated with models of aggressive behavior, a trait which the wolverine is purported to have in the popular literature. To support an integrated, genomics-based wildlife management strategy at a time of environmental disruption from climate change, we annotated the principal genes of the innate immune system to provide a resource to study the wolverine's susceptibility to new infectious and parasitic diseases. As a resource, we annotated genes involved in the modality of infection by the coronaviruses, an important class of viral pathogens of growing concern as shown by the recent spillover infections by severe acute respiratory syndrome coronavirus-2 to naïve wildlife. Tabulation of heterozygous single nucleotide variants in our specimen revealed a heterozygosity level of 0.065%, indicating a relatively diverse genetic pool that would serve as a baseline for the genomics-based conservation of the wolverine, a rare cold-adapted carnivore now under threat.
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Project description:Whole Genome Sequencing of the Wolverine Gulo gulo
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Project description:In conservation prioritisation, it is often implicit that representation targets for individual habitat types act as surrogates for the species that inhabit them. Yet for many commercially and ecologically important coral reef fish species, connectivity among different habitats in a seascape may be more important than any single habitat alone. Approaches to conservation prioritisation that consider seascape connectivity are thus warranted. I demonstrate an approach that can be implemented within a relatively data-poor context, using widely available conservation planning software. Based on clearly stated assumptions regarding species' habitat usage and movement ability, this approach can be adapted to different focal species and contexts, or refined as further data become available. I first derive a seascape connectivity metric based on area-weighted proximity between juvenile and adult habitat patches, and then apply this during spatial prioritisation using the decision-support software Marxan. Using a case study from Micronesia, I present two applications: first, to inform prioritisation for a network of marine protected areas to achieve regional objectives for habitat representation; and second, to identify nursery habitat patches that are most likely to supply juveniles to adult populations on reefs within existing protected areas. Incorporating seascape connectivity in conservation prioritisation highlights areas where small marine protected areas placed on coral reefs might benefit from proximity to other habitats in the seascape, and thus be more effective. Within the context of community tenure over resources, identification of critical nursery habitats to improve the effectiveness of existing marine protected areas indicates where collaboration across community boundaries might be required. Outputs from these analyses are likely to be most useful in regions where management is highly decentralised, imposing spatial constraints on the size of individual protected areas.
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Project description:BackgroundPublic and stakeholder engagement can improve the quality of both research and policy decision making. However, such engagement poses significant methodological challenges in terms of collecting and analysing input from large, diverse groups.ObjectiveTo explain how online approaches can facilitate iterative stakeholder engagement, to describe how input from large and diverse stakeholder groups can be analysed and to propose a collaborative learning framework (CLF) to interpret stakeholder engagement results.MethodsWe use 'A National Conversation on Reducing the Burden of Suicide in the United States' as a case study of online stakeholder engagement and employ a Bayesian data modelling approach to develop a CLF.ResultsOur data modelling results identified six distinct stakeholder clusters that varied in the degree of individual articulation and group agreement and exhibited one of the three learning styles: learning towards consensus, learning by contrast and groupthink. Learning by contrast was the most common, or dominant, learning style in this study.ConclusionStudy results were used to develop a CLF, which helps explore multitude of stakeholder perspectives; identifies clusters of participants with similar shifts in beliefs; offers an empirically derived indicator of engagement quality; and helps determine the dominant learning style. The ability to detect learning by contrast helps illustrate differences in stakeholder perspectives, which may help policymakers, including Patient-Centered Outcomes Research Institute, make better decisions by soliciting and incorporating input from patients, caregivers, health-care providers and researchers. Study results have important implications for soliciting and incorporating input from stakeholders with different interests and perspectives.
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Project description:Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters. The context of the annotated content is made explicit in a standard way by associating it with ontological terms (e.g., species, cell types, brain regions). The exact position of the annotated content within a document is specified by the starting character of the annotated text, or the number of the figure, the equation, or the table, depending on the context. Alternatively, the provenance of parameters can also be specified by bounding boxes. Parameter types are linked to curated experimental values so that they can be systematically integrated into models. We demonstrate the use of this approach by releasing a corpus describing different modeling parameters associated with thalamo-cortical circuitry. The proposed framework supports a rigorous management of large sets of parameters, solving common difficulties in their traceability. Further, it allows easier classification of literature information and more efficient and systematic integration of such information into models and analyses.
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Project description:UnlabelledClinical information systems (CIS) can affect the quality of patient care. In this paper, we focus on CIS use in the collaborative treatment of chronic diseases. We have developed a framework to determine which CIS functions have general usefulness for improving patient outcomes.MethodsWe reviewed the use of clinical information systems within a collaborative care environment, identifying CIS functions important in chronic disease care. We grouped the functions into categories of access, best practices, and communication (ABC). Three independent raters selected the most important collaborative care related functions from the HL7 Electronic Health Record Systems functional model, and mapped the HL7 functions against the ABC categories. We then built a model of CIS use and tested it on data from a cohort of patients with chronic illnesses.ResultsOf the 133 HL7 elements in the ABC model, 60 (45%) were ranked as important for collaborative care by two reviewers. Agreement was moderate for importance (kappa=.20) but high for ABC categorization (kappa=.67). In our data tests, for the 1105 patients, access 4.4+/-6.5, best practices 0.8+/-1.6, and communication 2.9+/-4.5. CIS functions were used per episode of care. We were able to identify several key functions that may affect patient care. For example, certain CIS functions related to best practices were associated with higher clinician adherence to testing guidelines.DiscussionThis framework may be useful to assess and compare CIS systems for collaborative care. Future refinements of the model are discussed.
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