Relevant Scales Research Articles

Multiomics Analysis Reveals Gut Virome-Bacteria-Metabolite Interactions and Their Associations with Symptoms in Patients with IBS-D.

The gut microbiota is involved in the pathogenesis of diarrhea-predominant irritable bowel syndrome (IBS-D), but few studies have focused on the role of the gut virome in IBS-D. We aimed to explore the characteristics of the gut virome in patients with IBS-D, its interactions with bacteria and metabolites, and the associations between gut multiomics profiles and symptoms. This study enrolled twelve patients with IBS-D and eight healthy controls (HCs). The stool samples were subjected to metavirome sequencing, 16S rRNA gene sequencing, and untargeted metabolomic analysis. The participants completed relevant scales to assess the severity of their gastrointestinal symptoms, depression, and anxiety. The results revealed unique DNA and RNA virome profiles in patients with IBS-D with significant alterations in the abundance of contigs from Siphoviridae, Podoviridae, Microviridae, Picobirnaviridae, and Tombusviridae. Single-omics co-occurrence network analyses demonstrated distinct differences in the gut virus, bacteria, and metabolite network patterns between patients with IBS-D and HCs. Multiomics networks revealed that short-chain fatty acid-producing bacteria occupied more core positions in IBS-D networks, but had fewer links to viruses. Amino acids and their derivatives exhibit unique connectivity patterns and centrality features within the IBS-D network. The gastrointestinal and psychological symptom factors of patients with IBS-D were highly clustered in the symptom-multiomics network compared with those of HCs. Machine learning models based on multiomics data can distinguish IBS-D patients from HCs and predict the scores of gastrointestinal and psychological symptoms. This study provides insights into the interactions among gut viruses, bacteria, metabolites, and clinical symptoms in patients with IBS-D, indicating further classification and personalized treatment for IBS-D.

Choice of reaction progress variable under preferential diffusion effects in turbulent syngas combustion based on detailed chemistry direct numerical simulations

The combustion of hydrogen and carbon-monoxide mixtures, so-called syngas, plays an increasingly important role in the safety context of non-fossil energy generation, more specifically in the risk management of incidents in process engineering plants for ammonia synthesis and in nuclear power plants. In order to characterize and simulate syngas/air combustion on industrially relevant scales, subgrid modelling is required, which is often based on a reaction progress variable. To understand the influence of different fuel compositions, turbulence intensities and flame topologies on different possible definitions of reaction progress variable, detailed chemistry direct numerical simulations data of premixed, lean hydrogen/air and syngas/air flames has been considered. A reaction progress variable based on normalized molecular oxygen mass fraction has been found not to capture the augmentation of the normalized burning rate per unit flame surface area in comparison to the corresponding 1D unstretched premixed flame due to preferential diffusion effects. By contrast, reaction progress variables based on other individual species, such as hydrogen, can capture the augmentation of the rate of burning well, but exhibit a pronounced sensitivity to preferential diffusion effects, especially in response to flame curvatures. However, a reaction progress variable based on the linear combination of the main products can accurately represent the temperature evolution of the flame for different mixtures, turbulence intensities and varying local flame topology, while effectively capturing the augmentation of burning rate due to preferential diffusion effects. However, its tendency to assume values larger than 1.0 in the regions of super-adiabatic temperatures poses challenges for future modeling approaches, whereas the reaction progress variable based on hydrogen mass fraction remains bound between 0.0 and 1.0 despite showing deviations in comparison to corresponding variations obtained from the unstretched laminar flame depending on flame curvature variations.

Positive effects of intercrop yields in farms from across Europe depend on rainfall, crop composition, and management

Modern “intensive” agriculture drives the biodiversity-climate crisis but is also central to global food security. Future farming needs management approaches that maintain (or even enhance) food production while reducing negative climate and biodiversity impacts. Intercrops could provide part of the solution, increasing biodiversity and boosting production with fewer inputs. However, barriers remain to their wide-scale uptake, in particular tailoring intercrops to local equipment, management practice, and environment. We analyze data from multiple trials of cereal-legume intercrops conducted on farms across Europe between 2018 and 2021. Our study is the first attempt, to our knowledge, to quantify the yield benefits of cereal-legume intercropping undertaken at commercially relevant scales for farms across Europe. We used crop performance ratio (CPR)—the ratio of the observed intercrop yield compared to the expected yield based on monoculture yields—as our metric of intercrop performance. Using CPR, we found a roughly 30% yield gain across all sites. However, CPR was modulated by a number of factors. CPR was not strongly affected by management except for the negative effects of direct drilling and the positive effects of organic fertilizer addition. CPR also depended on intercrop composition (number and identity of components), background yields (being highest where yields were lower), and rainfall (being higher with higher rainfall). Our findings allow us to reduce uncertainty about how intercrops will perform in realistic local farm conditions, give guidance for tailoring intercrops to local farming conditions, and provide key goals for further work to integrate intercrops into sustainable farming systems.

Perme ICU Physical Therapy Competency: Development of an ICU Knowledge and Skills Assessment Tool

BackgroundPhysical therapy for patients in the ICU is advanced practice demanding specialized knowledge and skills. However, ICU physical therapy competency standards lack uniformity or defined processes. ObjectivesTo describe the development process of the Perme ICU Physical Therapy Competency and to assess its face and content validity. MethodsQuantitative research study for the content validation of the Perme ICU Physical Therapy Competency using a panel of experts. The face validity assessment consisted of two informal surveys and discussions with clinicians representing various disciplines in ICU. Main Outcome MeasuresA content validation survey included analysis of sufficiency, clarity, coherence, and relevance for items in the Perme ICU Physical Therapy Competency. For the quantitative analysis of content validity, the item-level content validity index (I-CVI) was used. Scale-level content validity index based on the universal agreement method (S-CVI/UA) was calculated as the proportion of items on the scale that achieve a relevance scale of 3 or 4 by all experts. Scale-level content validity index was calculated based on the average method (S-CVI/Ave). ResultsThe sufficiency, clarity, coherence, and relevance of the Perme ICU Physical Therapy Competency items presented S-CVI/Ave greater than 80 % (97 %, 97 %, 99 %, 95 %, respectively). ConclusionThis study establishes that the Perme ICU Physical Therapy Competency has a satisfactory level of face and content validity. Implications for clinical practiceThe Perme ICU Physical Therapy Competency, with its solid framework, is a valuable assessment tool applicable for integration in any ICU competency program. It can be utilized as a self-assessment tool by individual therapists or in collaboration with mentors and evaluators to evaluate knowledge and skills effectively. This innovative tool not only enhances clinical practice but also presents an opportunity for advancing the physical therapy profession within the ICU setting.

Adapting the stroke therapy outcome measure for use by Multidisciplinary Teams: considering reliability

Purpose The Therapy Outcome Measure (TOM) comprises many profession and client group-specific adapted scales based on the International Classification of Functioning Model, used by different Health Care Professionals (HCPs). A new Multidisciplinary stroke TOM scale (MDT Stroke TOMs) was developed by amalgamating the relevant scales. This study aimed to develop a scale to describe patients’ abilities and improve communication and collaboration within the stroke Multidisciplinary Team (MDT). The present study investigated the inter-rater and intra-rater reliability of this adaptation. Materials and Methods The MDT Stroke TOM Scale was developed by combining and rewording four profession-specific adapted TOM scales following iterative trials and discussions leading to an agreed version. 150 stroke Allied Health Professional (AHPs) and nurses were trained virtually in TOM and the MDT Stroke TOM Scale. Ten AHP and nursing raters from five professions evaluated ten composite stroke case studies for inter-rater reliability. Six participated in the intra-rater reliability review of the same three case studies at different time points. Reliability was quantified using intraclass correlation coefficients and measurement error using the smallest detectable change. Results There was good to excellent concordance between the raters in terms of scoring. Raters’ scores were also consistent. The MDT Stroke TOMs is being used consistently across the South West London Stroke Network. Conclusion The Stroke MDT TOMs is psychometrically robust and should assist with improving team work in the rehabilitation of stroke patients and their carers. Future research may use MDT Stroke TOM to promote interprofessional team working and facilitate communication in Stroke Rehabilitation.

Development and prima facie validation of the Dementia-Specific Ethical Self-Efficacy scale for professional caregivers.

To develop and validate a scale for measuring professional caregivers' ethical self-efficacy in dementia care. Professional caregivers of people with dementia make ethical decisions on a day-to-day basis, and it is important that they feel confident when doing so. Moreover, confidence, or self-efficacy, influences caregivers' behaviour and well-being and may be a predictor of competence. However, there is no scale for measuring ethical self-efficacy. This study aims to fill this gap. This study concerns the development and prima facie validation of the Dementia-Specific Ethical Self-Efficacy (DemESE) scale. During development, we identified dementia-specific ethical principles and generated items representing ethical conflicts of principles. In the subsequent validation, we administered the scale to experts and professional caregivers in dementia care. We assessed the relevance of the scale using a content validity index and tested validity and reliability using Cronbach's alpha. To further enhance validity, we compared the scale with analogous self-efficacy scales using Pearson's correlation coefficient. The quantitative testing of DemESE revealed that the scale exhibited acceptable levels of internal consistency and reliability. This finding was supported by Cronbach's alpha. In addition, the content validity index and Pearson correlation coefficient provided evidence of the scale's relevance and validity. The results suggest that DemESE is a promising tool for assessing professional caregivers' ethical self-efficacy in dementia care and may be used to measure ethical self-efficacy - that is, confidence in ethical decision-making in dementia care.

Ecological scales of effect vary across space and time

The spatial scale at which an environmental variable is summarized can have considerable impacts on ecological inference of species distribution and abundance. While several analytical approaches have emerged to determine biologically relevant spatial scales – the spatial scale that most strongly influences the ecological patterns observed – identifying key ecological drivers of scale of effect is still underway. Additionally, several predicted ecological drivers of scale of effect can vary across space and time, but little research on spatiotemporal patterns has occurred. Here, I assessed spatial and temporal variation in scales of effect across 32 North American bird species using 18 years of empirical data from the North American breeding bird survey. Scale estimation was then coupled with trait‐based analyses and hypotheses testing of underlying processes of spatial and temporal variation in scales of effect. All 32 species tested exhibited varied scales of effect across years (average annual scales of effect ranging from 0.2 to 4.97 km) and Bird Conservation Regions (BCR), with spatial variability being the most pronounced. Trait‐based analyses revealed a contrary relationship between hand‐wing index, body size, and scale of effect, though the strength of this relationship was contingent on migratory status. Temporal variation in scales of effect was best explained by changes in human development over time, indicating that avian space use can be explained by an increasing human footprint. Additionally, relative population size, consistent with theoretical predictions stemming from density‐dependent population dynamics, explained notable variation in spatial and temporal scales of effect. These findings contribute to the growing landscape ecology literature by providing empirical evidence for hypothesized drivers of scales of effect. By delineating species‐specific scales of effect and elucidating their ecological drivers, this study enhances our understanding of spatial and temporal scales in ecological processes, aiding conservation efforts in a rapidly changing world.

Spatial and temporal variation in ambient vibratory noise and its impact potential on a common urban arthropod

AbstractRecent, rapid arthropod declines have been tied to human activity. Yet, the propensity for human‐produced noise to disturb the substrate‐borne (vibratory) channel remains virtually untested despite arthropods' ubiquitous use of vibratory information. Characterizing the vibratory landscape at biologically relevant scales is an essential initial step. We aimed to measure vibratory noise across space and time to assess its potential overlap with the urban–rural range and season of a funnel‐weaving spider, Agelenopsis pennsylvanica (C.L. Koch) (Araneae: Agelenidae), a common urban arthropod. We assessed 24‐h ambient vibrations (hereafter vibratory “noise”) at 23 sites across urban–rural Lincoln, Nebraska (NE, USA), during the adult season of A. pennsylvanica (August–October). Vibratory noise (amplitude, Leq) varied by ~15 dB across sites overlapping with frequencies within A. pennsylvanica's sensitivity (20–1000 Hz). Urban vibratory noise was positively correlated with the principal component containing estimates of traffic‐induced vibrations (e.g., traffic volume, road proximity, and impervious cover), whereas rural noise levels correlated with probable harvest times. Our findings indicate spatial variation in noise in urban areas and seasonal noise variability in rural areas. We also tested how A. pennsylvanica—collected from four distinct noise survey sites—use their vibratory microhabitat. We assessed daily spider position and dry silk mass within a microcosm that played loud and quiet vibrations (white noise differing by ~15 dB) in separate but connected chambers. Age affected the chamber choice (hereafter “microhabitat use”) of spiders collected from the loudest site, as younger adults associated with and used more silk in the loud chamber, but this effect decreased with age. As our data demonstrate that vibratory noise varies with A. pennsylvanica's microhabitat and season with a high potential for impacting behavior, we hypothesize that this environmental noise likely impacts other arthropods as well.

Large Language Models for Intraoperative Decision Support in Plastic Surgery: A Comparison between ChatGPT-4 and Gemini.

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models' response time. We compared the performance using the Mann-Whitney U test and Student's t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4's (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini's average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT'-4's (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

Kardar-Parisi-Zhang universality in two-component driven diffusive models: Symmetry and renormalization group perspectives.

We elucidate the universal spatiotemporal scaling properties of the time-dependent correlation functions in a class of two-component one-dimensional (1D) driven diffusive system that consists of two coupled asymmetric exclusion processes. By using a perturbative renormalization group framework, we show that the relevant scaling exponents have values same as those for the 1D Kardar-Parisi-Zhang (KPZ) equation. We connect these universal scaling exponents with the symmetries of the model equations. We thus establish that these models belong to the 1D KPZ universality class.

When income meets faith: the development and application of the Chinese generation Z unconventional religious orientation scale

This study seeks to analyze the psychological construction of Unconventional Religious Orientations and their association with individual income level satisfaction within Generation Z. Generation Z, individuals born between 1995 and 2010, grew up in a socio-cultural context marked by digitization and globalization. This study identifies three key dimensions of Unconventional Religious Orientations: religious spiritual dependence, religious instrumental tendencies, and religious uniqueness identity. By combining rootedness theory, semi-structured interviews, and literature review, we constructed and refined a set of relevant scales. Using exploratory and validation factor analyses (EFA and CFA), we verified the structural validity of the scale. The results of the analyses revealed significant negative correlations between satisfaction with income level and all dimensions of Unconventional Religious Orientation for Generation Z, suggesting that Unconventional Religious Orientation tends to diminish as income satisfaction increases. In addition, the significant positive correlations between these dimensions of religious inclination imply that they may share certain underlying factors in their psychological structure. This study not only successfully developed a set of psychometric instruments for Unconventional Religious Orientations, but also provided a new psychological perspective for understanding the dynamic interaction between economic satisfaction and religious psychological attitudes in Generation Z.

An evaluation of AI generated literature reviews in musculoskeletal radiology

PurposeThe use of artificial intelligence (AI) tools to aid in summarizing information in medicine and research has recently garnered a huge amount of interest. While tools such as ChatGPT produce convincing and naturally sounding output, the answers are sometimes incorrect. Some of these drawbacks, it is hoped, can be avoided by using programmes trained for a more specific scope. In this study we compared the performance of a new AI tool (the-literature.com) to the latest version OpenAI's ChatGPT (GPT-4) in summarizing topics that the authors have significantly contributed to. MethodsThe AI tools were asked to produce a literature review on 7 topics. These were selected based on the research topics that the authors were intimately familiar with and have contributed to through their own publications. The output produced by the AI tools were graded on a 1–5 Likert scale for accuracy, comprehensiveness, and relevance by two fellowship trained consultant radiologists. ResultsThe-literature.com produced 3 excellent summaries, 3 very poor summaries not relevant to the prompt, and one summary, which was relevant but did not include all relevant papers. All of the summaries produced by GPT-4 were relevant, but fewer relevant papers were identified. The average Likert rating was for the-literature was 2.88 and 3.86 for GPT-4. There was good agreement between the ratings of both radiologists (ICC = 0.883). ConclusionSummaries produced by AI in its current state require careful human validation. GPT-4 on average provides higher quality summaries. Neither tool can reliably identify all relevant publications.

Unveiling impacts: A quality of life survey for individuals with a history or diagnosis of cancer.

e23179 Background: Cancer treatments, despite increasing effectiveness, involve significant short and long-term side effects profoundly impacting a person's quality of life (QoL). In response, Advocates for Collaborative Education, a global coalition of patient advocates, initiated an exempt, online survey to gather insights on the perceived QoL impact of cancer diagnosis and treatment. The effort sought to identify community priorities and hopes to guide improvements in supportive care. Methods: A comprehensive survey, developed by patients, survivors, and advocates, comprised six sections and 93 questions. Co-creation played a vital role in determining relevant categories, questions, and measurement scales to assess severity and impact of QoL challenges. The study requested demographic and cancer diagnosis data before inquiring about 17 treatment-related side effects, their perceived severity, preparation, unintended side effects of supportive care, and personal preferences for cancer-related information. Recruitment was conducted via social media. Results: The survey garnered responses from 515 individuals with a history of cancer and a wide range of demographic subsets, including age, race, stage of cancer, and treatment modalities. Survey respondents reported an average pre-diagnosis QoL score of 8.87 out of 10, which declined to a mean score of 6.41 post-diagnosis. Across all cancer types, physical well-being was a top priority for most respondents (51.5%), followed by emotional well-being (25.4%). Across a variety of side effects, unintended consequences of side effect treatments were highlighted, revealing complexities in supportive care. Detailed results for all categories and stratification by subsets of age, race, metastatic vs. early stage, treatment type, and others are available for presentation and publication purposes. Conclusions: This study highlights the significant negative impact of cancer treatment on an individual's QoL, addressing an underexplored area of cancer research. While managing treatment side effects is crucial, supportive therapies may introduce additional side effects, exacerbating an overall burden on patients. The collaborative survey development with patients and survivors represents a pivotal step, offering valuable insights for enhancing the well-being of individuals affected by cancer. [Table: see text]

High-cell-density fed-batch strategy to manufacture tailor-made P(HB-co-HHx) by engineered Ralstonia eutropha at laboratory scale and pilot scale.

The transition towards a sustainable bioeconomy requires the development of highly efficient bioprocesses that enable the production of bulk materials at a competitive price. This is particularly crucial for driving the commercialization of polyhydroxyalkanoates (PHAs) as biobased and biodegradable plastic substitutes. Among these, the copolymer poly(hydroxybutyrate-co-hydroxyhexanoate) (P(HB-co-HHx)) shows excellent material properties that can be tuned by regulating its monomer composition. In this study, we developed a high-cell-density fed-batch strategy using mixtures of fructose and canola oil to modulate the molar composition of P(HB-co-HHx) produced by Ralstonia eutropha Re2058/pCB113 at 1-L laboratory scale up to 150-L pilot scale. With cell densities >100 g L-1 containing 70-80 wt% of PHA with tunable HHx contents in the range of 9.0-14.6 mol% and productivities of up to 1.5 g L-1 h-1, we demonstrate the tailor-made production of P(HB-co-HHx) at an industrially relevant scale. Ultimately, this strategy enables the production of PHA bioplastics with defined material properties on the kilogram scale, which is often required for testing and adapting manufacturing processes to target diverse applications.

Sleep in the wild: the importance of individual effects and environmental conditions on sleep behaviour in wild boar.

Sleep serves vital physiological functions, yet how sleep in wild animals is influenced by environmental conditions is poorly understood. Here we use high-resolution biologgers to investigate sleep in wild animals over ecologically relevant time scales and quantify variability between individuals under changing conditions. We developed a robust classification for accelerometer data and measured multiple dimensions of sleep in the wild boar (Sus scrofa) over an annual cycle. In support of the hypothesis that environmental conditions determine thermoregulatory challenges, which regulate sleep, we show that sleep quantity, efficiency and quality are reduced on warmer days, sleep is less fragmented in longer and more humid days, while greater snow cover and rainfall promote sleep quality. Importantly, this longest and most detailed analysis of sleep in wild animals to date reveals large inter- and intra-individual variation. Specifically, short-sleepers sleep up to 46% less than long-sleepers but do not compensate for their short sleep through greater plasticity or quality, suggesting they may pay higher costs of sleep deprivation. Given the major role of sleep in health, our results suggest that global warming and the associated increase in extreme climatic events are likely to negatively impact sleep, and consequently health, in wildlife, particularly in nocturnal animals.

Quality of life in individuals with neurofibromatosis type 1 associated cutaneous neurofibromas: validation of the Dutch cNF-Skindex

BackgroundAlmost all patients with Neurofibromatosis type 1 (NF1) develop cutaneous neurofibroma (cNF), benign dermal tumours that have a large impact on the patient’s Quality of Life (QoL). The French cNF-Skindex is the first questionnaire to specifically assess cNF-related QoL in patients with NF1. We aimed to adapt and validate a Dutch version of the cNF-Skindex.MethodsThe questionnaire was translated using forward and backwards translation, and subsequently administered to a sample of 59 patients on two separate occasions. Feasibility was evaluated by the presence of floor/ceiling effects. Reliability was assessed by evaluating internal consistency and test-retest reliability, by calculating Cronbach’s alpha and Spearman’s rank correlation coefficients. The EQ-5D-5L and SF-36 were used to evaluate convergent validity, using Spearman’s rank correlation coefficients. An exploratory factor analysis was performed to study the data’s internal structure. Multivariable linear regression was used to model the relationship between patient characteristics and the cNF-Skindex.ResultsThe Dutch cNF-Skindex demonstrated excellent feasibility and reliability (Cronbach’s alpha 0.96, test-retest correlation coefficient 0.88). Convergent validity was confirmed for the EQ-5D-5L and relevant SF-36 scales. All items and subdomains from the original questionnaire were confirmed following exploratory factor analysis. The patient characteristics included in the multivariable linear regression were not significantly associated with the cNF-Skindex score.ConclusionsThe Dutch cNF-Skindex displayed excellent psychometric properties, enabling use in the Netherlands.

Formamide denaturation of double-stranded DNA for fluorescence in situ hybridization (FISH) distorts nanoscale chromatin structure.

As imaging techniques rapidly evolve to probe nanoscale genome organization at higher resolution, it is critical to consider how the reagents and procedures involved in sample preparation affect chromatin at the relevant length scales. Here, we investigate the effects of fluorescent labeling of DNA sequences within chromatin using the gold standard technique of three-dimensional fluorescence in situ hybridization (3D FISH). The chemical reagents involved in the 3D FISH protocol, specifically formamide, cause significant alterations to the sub-200 nm (sub-Mbp) chromatin structure. Alternatively, two labeling methods that do not rely on formamide denaturation, resolution after single-strand exonuclease resection (RASER)-FISH and clustered regularly interspaced short palindromic repeats (CRISPR)-Sirius, had minimal impact on the three-dimensional organization of chromatin. We present a polymer physics-based analysis of these protocols with guidelines for their interpretation when assessing chromatin structure using currently available techniques.

Unraveling the characteristic spatial scale of habitat selection for forest grouse species in the boreal landscape

The characteristic spatial scale at which species respond strongest to forest structure is unclear and species-specific and depends on the degree of landscape heterogeneity. Research often analyzes a pre-defined spatial scale when constructing species distribution models relating forest variables with occupancy patterns. This is a limitation, as forest characteristics shape the species use of habitat at multiple spatial scales. To explore the drivers of this relationship, we conducted an in-depth investigation into how scaling forest variables at biologically relevant spatial scales affects occupancy of grouse species in boreal forest. We used 4,790 grouse observations (broods and adults) collected over 39,303 stands for 15 years of four forest grouse species (capercaillie, black grouse, hazel grouse, and willow grouse) obtained from comprehensive Finnish wildlife triangle census data and forest variables obtained from Airborne Laser Scanning and satellite data originally sampled at 16 m resolution. We fitted Generalized Additive Mixed Models linking grouse presence/absence in the Finnish boreal forest with forest stand structure and composition. We estimated the effects of predictor variables aggregated at three spatial scales reflecting the species use of the landscape: local level at stand scale, home range level at 1 km radius, and regional level at 5 km radius. Multi-grain models considering forest-species relationships at multiple scales were used to evaluate whether there is a specific scale at which forest characteristics best predict local grouse occupancy. We found that that the spatial scale affected the predictive capacity of the grouse occupancy models and the characteristic scale of habitat selection was the same (i.e., stand scale) among species. Different grouse species exhibited varying optimal spatial scales for occupancy prediction. Forest structure was more important than compositional diversity in predicting grouse occupancy irrespective of the scale. A limited number of forest predictors related to availability of multi-layered vegetation and of suitable thickets explained the occupancy patterns for all the grouse species at different scales. In conclusion, modeling grouse occupancy using forest predictors at different spatial scales can inform forest managers about the scale at which the species perceive the landscape. This evidence calls for an integrated multiscale approach to habitat modelling for forest species.

Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands

ContextDynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging.ObjectivesWe identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment.MethodsUsing high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance.ResultsWe found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R2 = 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales.ConclusionsOur results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems.

Numerical linked-cluster expansions for two-dimensional spin models with continuous disorder distributions.

We show that numerical linked cluster expansions (NLCEs) based on sufficiently large building blocks allow one to obtain accurate low-temperature results for the thermodynamic properties of spin lattice models with continuous disorder distributions. Specifically, we show that such results can be obtained computing the disorder averages in the NLCE clusters before calculating their weights. We provide a proof of concept using three different NLCEs based on L, square, and rectangle building blocks. We consider both classical (Ising) and quantum (Heisenberg) spin-1/2 models and show that convergence can be achieved down to temperatures that are up to two orders of magnitude lower than the relevant energy scale in the model. Additionally, we provide evidence that in one dimension one can obtain accurate results for observables such as the energy down to their ground-state values.