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ObjectivesPersistent dysfunctional expectations seem to be core features of mental disorders. The aim of this study was to develop a questionnaire that assesses mechanisms responsible for the consistency of dysfunctional expectations. Processes before (i.e., assimilation) and after (i.e., immunization) expectation-violating experiences have been considered.DesignThe Immunization Scale (IMS) is constructed and validated with the help of an explorative (EFA) and confirmatory factor analysis (CFA) in two conducted studies.Materials and methodsFor the first study, the initially formulated 75-item version was completed online by 230 (range 18–69) participants from a convenience sample. For the second study, 299 (range 18–62) participants completed the reduced scale at the first measurement point, 75 participants thereof also 1 month later. For validity and reliability analyses, participants in both studies provided demographic information, the Beck Depression Inventory (BDI-II), the Depressive Expectation Scale (DES), the Beck Anxiety Inventory (BAI), and the German version of the Acceptance and Action Questionnaire (FAH-II).ResultsThe initial 75 items were reduced to 23 items. The EFA revealed three main factors, namely, negative expectations, assimilation, and cognitive immunization. The three-factor structure could be confirmed in study 2 by the CFA. Reliability measures showed an excellent internal consistency for the entire IMS. A very good test–retest reliability was found. Significant correlations resulted between the IMS and DES, BDI-II, BAI, and FAH-II, the highest for DES and FAH-II.ConclusionPsychometric properties of the IMS are promising. Future studies should verify the reliability and validity measures in other population samples. The IMS can be very useful in expectation research, especially in the examination of expectation-focused therapy.
ObjectivesPersistent dysfunctional expectations seem to be core features of mental disorders. The aim of this study was to develop a questionnaire that assesses mechanisms responsible for the consistency of dysfunctional expectations. Processes before (i.e., assimilation) and after (i.e., immunization) expectation-violating experiences have been considered.DesignThe Immunization Scale (IMS) is constructed and validated with the help of an explorative (EFA) and confirmatory factor analysis (CFA) in two conducted studies.Materials and methodsFor the first study, the initially formulated 75-item version was completed online by 230 (range 18–69) participants from a convenience sample. For the second study, 299 (range 18–62) participants completed the reduced scale at the first measurement point, 75 participants thereof also 1 month later. For validity and reliability analyses, participants in both studies provided demographic information, the Beck Depression Inventory (BDI-II), the Depressive Expectation Scale (DES), the Beck Anxiety Inventory (BAI), and the German version of the Acceptance and Action Questionnaire (FAH-II).ResultsThe initial 75 items were reduced to 23 items. The EFA revealed three main factors, namely, negative expectations, assimilation, and cognitive immunization. The three-factor structure could be confirmed in study 2 by the CFA. Reliability measures showed an excellent internal consistency for the entire IMS. A very good test–retest reliability was found. Significant correlations resulted between the IMS and DES, BDI-II, BAI, and FAH-II, the highest for DES and FAH-II.ConclusionPsychometric properties of the IMS are promising. Future studies should verify the reliability and validity measures in other population samples. The IMS can be very useful in expectation research, especially in the examination of expectation-focused therapy.
Accurate and up‐to‐date land cover maps are vital for underpinning evidence‐based landscape management decision‐making. However, the technical skills required to extract tailored information about land cover dynamics from these open‐access geospatial data often limit their use by those making landscape management decisions. Using Dartmoor National Park as an example, we demonstrate an open‐source toolkit which uses open‐source software (QGIS and RStudio) to process freely available Sentinel‐2 and public LiDAR data sets to produce fine scale (10 m2 grain size) land cover maps. The toolbox has been designed for use by staff within the national park, for example, enabling land cover maps to be updated as required in the future. An area of 945 km2 was mapped using a trained random forest classifier following a classification scheme tailored to the needs of the national park. A 2019 land cover map had an overall user's accuracy of 79%, with 13 out of 17 land cover classes achieving greater than 70% accuracy. Spatially, accuracy was related via logistical regression to blue band surface reflectance in the spring and topographic slope derived from LiDAR (1 m resolution), with greater accuracy in steeper terrain and areas exhibiting higher blue reflectance. Between an earlier (2017–2019) and later (2019–2021) time frame, 8% of pixels changed, most of the change by area occurred in the most common classes. However, the largest proportional increase occurred in Upland Meadows, Lowland Meadows and Blanket Bog, all habitats subject to restoration efforts. Identifying areas of change enables future field work to be better targeted. We discuss the application of this mapping to land management within the Dartmoor national park and of the potential of tailored land cover and land cover change mapping, via this toolbox, to evidence‐based environmental decision‐making more widely.
Premise Living collections maintained for generations are at risk of diversity loss, inbreeding, and adaptation to cultivation. To address these concerns, the zoo community uses pedigrees to track individuals and implement crosses that maximize founder contributions and minimize inbreeding. Using a pedigree management approach, we demonstrate how conducting strategic crosses can minimize genetic issues that have arisen under current practices. Methods We performed crosses between collections and compared progeny fitness, including plant performance and reproductive health. We genotyped the progeny and parental accessions to measure changes in diversity and relatedness within and between accessions. Results The mean relatedness values among individuals within each accession suggest they are full siblings, demonstrating that there was high inbreeding and low diversity within accessions, although less so among accessions. Progeny from the wider crosses had increased genetic diversity and were larger and more fertile, while self‐pollinated accessions were smaller and less fertile. Discussion Institutions that hold exceptional species should consider how diversity is maintained within their collections. Implementing a pedigree‐based approach to managing plant reproduction ex situ will slow the inevitable loss of genetic diversity and, in turn, result in healthier collections.
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