Scaling Considerations Research Articles

Toward a complete interdisciplinary treatment of scale

The pathways taken throughout any model-based process are undoubtedly influenced by the modeling team involved and the decision choices they make. For interconnected socioenvironmental systems (SES), such teams are increasingly interdisciplinary to enable a more expansive and holistic treatment that captures the purpose, the relevant disciplines and sectors, and other contextual settings. In practice, such interdisciplinarity increases the scope of what is considered, thereby increasing choices around model complexity and their effects on uncertainty. Nonetheless, the consideration of scale issues is one critical lens through which to view and question decision choices in the modeling cycle. But separation between team members, both geographically and by discipline, can make the scales involved more arduous to conceptualize, discuss, and treat. In this article, the practices, decisions, and workflow that influence the consideration of scale in SESs modeling are explored through reflexive accounts of two case studies. Through this process and an appreciation of past literature, we draw out several lessons under the following themes: (1) the fostering of collaborative learning and reflection, (2) documenting and justifying the rationale for modeling scale choices, some of which can be equally plausible (a perfect model is not possible), (3) acknowledging that causality is defined subjectively, (4) embracing change and reflection throughout the iterative modeling cycle, and (5) regularly testing the model integration to draw out issues that would otherwise be unnoticeable.

A procedure to develop a backbone ground-motion model: A case study for its implementation

The backbone modeling in ground-motion characterization (GMC) is a useful methodology to describe the epistemic uncertainty in median ground-motion predictions. The approach uses a backbone ground-motion model (GMM) and populates the GMC logic tree with the scaled and/or adjusted versions of the backbone GMM to capture the epistemic uncertainty in median ground motions. The scaling and/or adjustment should represent the specific features and uncertainties involved in source, path, and site effects at the target site. The identification of the backbone model requires different considerations specific to the nature of the ground-motion hazard problem. In this article, we present a scaled backbone modeling approach that considers the magnitude- and distance-scaling predictors as well as their correlation to address the epistemic uncertainty in median ground-motion predictions. This approach results in a trivariate normal distribution to fully define a range of epistemic uncertainty in a model sample space. The simultaneous consideration of magnitude and distance scaling while defining the epistemic uncertainty and the methodology followed for the simplified representation of trivariate normal distribution in ground-motion logic tree are the two important features in our procedure. We first present the proposed approach that is followed by a case study for Central and Eastern North America (CENA) stable continental region. The case study discusses the underlying assumptions and limitations of the proposed approach.

A framework for quantifying hydrologic effects of soil structure across scales

Earth system models use soil information to parameterize hard-to-measure soil hydraulic properties based on pedotransfer functions. However, current parameterizations rely on sample-scale information which often does not account for biologically-promoted soil structure and heterogeneities in natural landscapes, which may significantly alter infiltration-runoff and other exchange processes at larger scales. Here we propose a systematic framework to incorporate soil structure corrections into pedotransfer functions, informed by remote-sensing vegetation metrics and local soil texture, and use numerical simulations to investigate their effects on spatially distributed and areal averaged infiltration-runoff partitioning. We demonstrate that small scale soil structure features prominently alter the hydrologic response emerging at larger scales and that upscaled parameterizations must consider spatial correlations between vegetation and soil texture. The proposed framework allows the incorporation of hydrological effects of soil structure with appropriate scale considerations into contemporary pedotransfer functions used for land surface parameterization.

BIM-enabled multi-level assessment of age-friendliness of urban housing based on multiscale spatial framework: enlightenments of housing support for “aging-in-place”

The worldwide population of the elderly has increased rapidly. Housing issue is regarded as the main barrier to implementing “aging-in-place”, which is a recommended strategy to encourage the elderly to age in their residences. Although many studies have contributed to age-friendly cities and communities, few studies have focused on measuring housing age-friendliness with consideration of urban spatial scales. This study proposes a multiscale spatial framework of housing and develops a multi-level assessment of housing age-friendliness based on literature review and fuzzy-analytic hierarchy process (AHP). The multi-level assessment of age-friendliness takes advantage of building information modeling (BIM) to simplify the computation process of the housing age-friendliness index (HAFI). The feasibility of BIM-enabled multi-level assessment is proved by comparative analysis of two housing examples. Results of HAFI and sub-HAFIs indicate housing may perform variously with spatial levels. This study contributes to developing the age-friendly performance assessment for housing by considering various significances of housing characteristics in multiple spatial levels. The BIM-enabled multi-level assessment is an effective tool to help the elderly choose more appropriate housing, assist the government in allocating suitable public housing for aged applicants, and provide property developers and local governments with additional guidelines for housing design and urban renewal.

The Contour to Classification Game

The Contour to Classification game is a browser-based game that teaches middle school students basic concepts in supervised learning. The game is an online variant of the Neural Network game that was presented at AAAI Fall Symposium Teaching AI in K-12 track in 2019. We share preliminary findings from implementing the online version of the original Neural Network game in a pilot research study and describe the game’s evolution to the Contour to Classification game. The new game uses a simulation of a neural network to engage students, through digital drawing and selection interactions, in the classification of images. The players act as nodes in a multi-step process of compositing salient smaller features to form larger features and ultimately a partial contour of an object that is used to make a prediction. After evaluating the prediction, information is sent back through the network in processes mimicking back propagation and gradient descent. Additional rounds of the game can be played to witness how the network evolves and gets “better” at classifying images from contours. Through this game, we aimed for students to learn the structure, components, and functioning of a neural network, and the processes involved in supervised learning. The Contour to Classification game supports online student learning by providing the image classification experience using purely visual inputs to each layer. We will conclude with a discussion of if and how the evolving design addresses classroom needs and scaling considerations.

Meat substitution in burgers: nutritional scoring, sensorial testing, and Life Cycle Assessment

Substitution of beef with alternative proteins is one practical trend taken by industry and consumers to reduce the negative impact of convenience products on the environment. Numerous products based on plant, insect and fungi proteins compete to replace beef burgers in an environmentally friendly and healthy way. At the same time, there is a lack of studies which assess different options from environmental impact perspective but also with consideration of production scales, recipes, nutritional values, and sensory properties. Therefore, the current study aimed to perform a holistic assessment (Life Cycle Assessment, sensory properties, and nutritional profile) of beef burgers in comparison to selected, alternative burgers, available on the market in Germany. The results indicated that alternative burgers based on plant, insect and mycoprotein biomass would be more environmentally friendly than beef burgers, but only a couple of them (insect-based and soy-based) have satisfying sensory or nutritional properties. Pea-based and mycoprotein-based burgers were perceived as average burgers from an environmental and sensorial perspective. The study demonstrated a sustainability differentiation system of meat analogs in convenience products using the multicriteria framework with inclusion of nutrient scoring, sensorial testing and Life Cycle Assessment.

Science-Narrative Explorations of “Drought Thresholds” in the Maritime Eden Catchment, Scotland: Implications for Local Drought Risk Management

Drought in the United Kingdom is a “hidden” pervasive risk, defined and perceived in different ways by diverse stakeholders and sectors. Scientists and water managers distinguish meteorological, agricultural, hydrological, and socio-economic drought. Historically triggers in drought risk management have been demarcated solely in specialist hydrological science terms using indices and critical thresholds. This paper explores “drought thresholds” as a bridging concept for interdisciplinary science-narrative enquiry. The Eden catchment, Scotland acts as an exemplar, in a maritime country perceived as wet. The research forms part of creative experimentation in science-narrative methods played out in seven United Kingdom case-study catchments on hydro-meteorological gradients in the Drought Risk and You (DRY) project, with the agricultural Eden the most northerly. DRY explored how science and stories might be brought together to support better decision-making in United Kingdom drought risk management. This involved comparing specialist catchment-scale modelling of drought risk with evidence gathered from local narratives of drought perceptions/experiences. We develop the concept of thresholds to include perceptual triggers of drought awareness and impact within and between various sectors in the catchment (agriculture, business, health and wellbeing, public/communities, and natural and built environments). This process involved developing a framework for science-narrative drought “threshold thinking” that utilizes consideration of severity and scale, spatial and temporal aspects, framing in terms of enhancing or reducing factors internal and external to the catchment and new graphical methods. The paper discusses how this extended sense of thresholds might contribute to research and practice, involving different ways of linking drought severity and perception. This has potential to improve assessment of sectoral vulnerabilities, development of adaptive strategies of different stakeholders, and more tailored drought communication and messaging. Our findings indicate that drought risk presents many complexities within the catchment, given its cross-sectoral nature, rich sources of available water, variable prior drought experience among stakeholders, and different quantitative and perceptual impact thresholds across and within sectors. Fuzziness in identification of drought thresholds was multi-faceted for varied reasons. Results suggest that a management paradigm that integrates both traditional and non-traditional “fuzzy” threshold concepts across sectors should be integrated into current and future policy frameworks for drought risk management.

Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series

Spatio-temporal patterns of human activities can be affected by events, such as extreme weather. Events cause anomalies that could be expressed by abnormal activity patterns deviating from the inherent ones. The detection of spatio-temporal anomalies thus helps to understand the implicit influencing mechanism with which the external factors affect human activities. Existing methods of spatio-temporal anomaly detection usually treat the temporal information as attributes of spatial units, which is an over-simplification as it ignores complex temporal patterns (e.g., periodic components of time-series). Moreover, as the spatio-temporal resolutions affect expressed characteristics of anomalies, the sensitivity of anomalies to scale is also worth investigating. This study intends to detect and interpret the spatio-temporal anomalies of human activities from a multi-scale perspective. Being different from the single-scale consideration and independent consideration of multiple scales, this research investigates how the anomalies' characteristics change at multiple scales by anomaly matching. The criteria of anomaly matching are the overlapping degree of spatio-temporal influence ranges of anomalies. It helps to specify the events that caused the expressed anomalies. Besides, we introduce the time-series decomposition methods to decompose complex temporal patterns, highlighting the abnormal changes in activity patterns. The study is validated using a multi-temporal-scale simulation experiment, and a multi-spatial-scale experiment based on taxi data in Beijing. Results show that the multi-scale method can detect various anomalies. Moreover, obtained multi-scale characteristics of anomalies are easy to compare with external data, and thus benefit anomaly interpretation (validated by two sample anomalies). This study highlights the significance of scales in anomaly detection of human activities and provides references for related works.

Fine-scale habitat selection limits trade-offs between foraging and temperature in a grassland bird

AbstractMany species are frequently faced with the decision about how to balance the use of thermal refuge against access to food resources. We evaluated the habitat use of female greater prairie chickens (Tympanuchus cupido) to assess the potential for trade-offs between thermal conditions and food resources during the habitat selection process. Our objectives were to 1) compare near-ground temperatures, invertebrate availability, and vegetation characteristics at sites used by greater prairie chickens to conditions at random landscape locations in various time since fire patches and 2) assess changes in conditions at used sites throughout the day to determine if selection for resources changes relative to ambient conditions, resulting in trade-offs between foraging sites and thermal refuge. We found that greater prairie chickens primarily used patches 0–12 months postfire that had relatively high abundances and biomasses of invertebrates compared to other time since fire patches. Greater prairie chickens further modified their selection at relatively fine spatial scales within these food-rich patches to select for areas with cooler temperatures during the hottest part of the day. The use of thermal refuge did not appear to influence the access to food resources as invertebrate abundance and biomass at used sites were consistent throughout the day. Our results show that food resources and thermal cover influences habitat selection for greater prairie chickens, but there was little evidence for trade-offs during the habitat selection process. Consideration of spatial and temporal scales is critical for evaluating trade-offs in habitat selection for animals and this research provides insights into the decision-making process by prairie chickens.

RETRACTED ARTICLE: Intelligent recommendation method integrating knowledge graph and Bayesian network

In recent years, the amount of Internet information data has exploded, and the problem of “information overload” has become a huge challenge for Internet development. Through the prior knowledge of the known data distribution, combined with sample training data to estimate the mathematical model of the overall data. Intelligent recommendation methods based on knowledge graphs and Bayesian networks are a hot spot in the current Internet research, and they are of great significance to search engines and e-commerce. This paper studies the fusion of knowledge graph and Bayesian belief network and its application in personalized recommendation and designs a Bayesian network that describes the purchase behavior of users based on the purchase records of customers across the network. User recommendation is the process of fusing knowledge graphs and Bayesian network reasoning. For the design of the recommendation network, a random reasoning algorithm with approximate reasoning is selected in consideration of calculation scale and accuracy. This paper uses the content filtering technology of KNN algorithm to supplement the sparse matrix, which can improve the quality of collaborative filtering recommendation, thereby improving the quality of final recommendation. In this paper, based on the knowledge graph, the EPCA algorithm and the robust recommendation algorithm are merged to form a robust collaborative recommendation model based on Bayesian probability matrix decomposition. The robust recommendation algorithm further improves the robustness of the recommendation algorithm by introducing the suspected users and target items detected by the EPCA algorithm. Research shows that the PS value of the VarSelect SVD algorithm used in this paper is approximately between 0.36 and 1.04. Compared with the first two algorithms, the PS value of VarSelect SVD has been significantly reduced, and the algorithm robustness has been significantly improved.

Cutting through the biofuel confusion: A conceptual framework to check the feasibility, viability and desirability of biofuels

Biofuels represent a complex issue in the sustainability discourse as they require the simultaneous consideration of different dimensions and scales of analysis. This situation explains the co-existence of contrasting ‘scientific evidence’ about their performance. This paper presents a novel conceptual framework that integrates four key aspects of the performance of biofuels: (1) the social factors determining the desirability of biofuel use on the demand side – why do we want to produce biofuels?; (2) the internal technical and economic constraints affecting the viability of their mode of production on the supply side – how can we produce biofuels?; (3) the external biophysical constraints limiting the feasibility of their production – what are the material limits imposed by the availability of natural resources?; (4) the level of openness of the biofuel system referring to the imports used to overcome local limits – the level of externalization of the requirement of natural resources and technical production factors reducing energy security. The proposed framework generates a biophysical characterization of the supply function of a biofuel system (which inputs are needed to generate the supply) contextualized against a biophysical characterization of the societal demand (what inputs the society is ready to invest in the energy system in order to obtain the supply).

Toward cross‐realm management of coastal urban ecosystems

Urbanization and its resulting environmental stressors are often managed within terrestrial, freshwater, or marine realms separately. This is problematic for the many cities situated at the intersection between these realms, because stressors and associated management actions can affect multiple realms simultaneously and impacts may be compounded at boundaries. Here, we describe management approaches operating within and across realms, and identify impediments to cross‐realm urban management. We find that management that fails to consider connectivity either within or between realms at best delivers only small‐scale benefits and at worst can produce large‐scale unintended negative outcomes. In contrast, management that explicitly considers inter‐realm connectivity can generate large benefits relative to the cost of interventions. Successful cross‐realm management requires consideration of connectivity, scale (temporal and spatial), and ecological processes across more than two realms, and is underpinned by an integrated and collaborative approach that provides multiple benefits.

A complex systems view of climate and development issues in South African coal power expansion

The implementation of climate change policy in South Africa inevitably requires decision-makers to navigate issues of development. This paper explores some of the implications of this requirement by examining the case of a proposed new independent coal-fired power producing plant, Khanyisa, in the province of Mpumalanga from the perspective of complexity studies, an emerging transdisciplinary field. Complexity thinking re-casts the Khanyisa project in a whole-system view, encouraging an active consideration of scale, perspectives, different knowledges, and cumulative impacts. In so doing, tensions both between and within dimensions of climate mitigation and development are quickly revealed, a complexity which is theorised in complexity studies as the raw material for systemic transformation. This whole-system conceptualisation also undermines incremental and relative arguments that Khanyisa mitigates greenhouse gas emissions. Further, the complex systemic property of non-linearity suggests that the Khanyisa decision is more significant than its power generation capacity indicates. Attention to the conceptual simplification inherent in ‘development’ highlights what is lost through such simplification, as well as what is gained, and by whom. Finally, complexity thinking foregrounds the multiple scales at which the systemic climate mitigation and development implications of Khanyisa play out. Currently there is very little policy-making capacity nationally, regionally or in eMalahleni to look at alternatives, or ‘spaces of possibility’ through the complexity lens for both development and climate mitigation. This case argues that new policy processes are needed, which go far beyond policy and regulatory processes steeped in path dependencies and incrementalism.
 Highlights• The case reveals the complex entanglement of climate and development issues as raw material for systemic transformation. • A whole system and scalar conceptualisation, paying attention to non-linearities, and the exercise of power through simplifications suggest productive areas of focus for policymakers• New policy processes are needed, which go far beyond policy and regulatory processes steeped in path dependencies and incrementalism.

Effectiveness of a nurse-led temporal self-regulation theory-based program on heart failure self-care: A randomized controlled trial

BackgroundHeart failure self-care is crucial for sustainable heart failure management but its adherence remains poor worldwide. Despite having an intention to change, individuals often face difficulties in modifying existing lifestyle habits and sustaining change motivations. ObjectivesTo examine the effectiveness of a novel theory-driven nurse-led self-regulation program on improving heart failure self-care behaviours, future-thinking and behavioural automaticity. DesignA two-arm randomized controlled trial. Settings & Participants144 patients with heart failure were recruited from September 2018 to July 2019 at a tertiary hospital in Singapore. MethodsParticipants were randomly assigned to a self-regulation intervention (n = 72) or usual care group (n = 72). The three-month intervention was developed based on the temporal self-regulation theory and consisted of one face-to-face session, a print booklet and three reinforcement telephone follow-ups at week 3, 6 and 9. Outcomes were measured at baseline (T0), immediate after a three-month intervention (T1) and a further three-month follow-up (T2). heart failure self-care was measured using the Self-Care of Heart Failure Index (SCHFI) maintenance subscale, future-thinking was measured using the Consideration of Future Consequences Scale (CFCS) and behaviour automaticity was measured using the Self-Reported Behavioural Automaticity Index (SRBAI). The outcomes were compared between groups by using generalized estimating equations (GEE) based on intention-to-treat principle. ResultsNo significant differences were found between the groups at baseline except for age. Participants were on average 61 years old, men (79.2%), had mild heart failure symptoms (50.7%) and had three comorbidities (66.0% dyslipidaemia; 65.3% hypertension; 61.8% history of myocardial infarction). Baseline scores indicated poor heart failure self-care (52.9±17.2, cut off ≥70). GEE results showed significantly higher heart failure self-care improvements in intervention group than control group at both T1 (regression coefficient, B = 13.9, 95% CI: 8.02 to 19.9, p < 0.001) and T2 (B = 8.34, 95% CI: 1.68 to 15.0, p = 0.014) after adjusting for gender, living alone, education level, comorbidity and age. Results also showed significantly higher increase in future-thinking (B[95% CI]=0.694[.123, 1.26], p = 0.017) and behaviour automaticity (B[95% CI]=0.656[.085, 1.23], p = 0.024) at T1 and only increase in behaviour automaticity (B[95% CI]=0.674[.099, 1.25], p = 0.022) at T2. However, only the differences in self-care scores at T1 remained significant after Bonferroni correction. No significant differences were found for intention, quality of life and clinical biomarkers. ConclusionsThe program was effective in improving heart failure self-care and has potential for clinical implementation and generalisation to other chronic illnesses. Longer follow-up study is needed to uncover its long-term benefits on clinical outcomes.

Impact of explicit area scaling on kinetic models involving multiple compartments

BackgroundComputational modelling of cell biological processes is a frequently used technique to analyse the underlying mechanisms and to generally understand the behaviour of these processes in the context of a pathway, network or even the whole cell. The most common technique in this context is the usage of ordinary differential equations that describe the kinetics of the relevant processes in mechanistic detail. Here, it is usually assumed that the content of the cell is well-stirred and thus homogeneous - which is of course an over-simplification, but often worked in the past. However, many processes happen at membranes and thus not in 3D, but in 2D. The scaling of the rates of these processes poses a special problem, if volumes of compartments are changed. They will typically scale with an area, but not with the volume of the involved compartment. However, commonly, this is neglected when setting up models and/or volume scaling also sometimes automatically happens when using modelling software in the field.ResultsHere, we investigate generic as well as specific, realistic cases to find out, how strong the impact of the wrong scaling is for the outcome of simulations. We show that the importance of correct area scaling depends on the architecture of the reaction site and its changes upon volume alterations and it is hard to foresee, if it has a significant impact or not just by looking at the original model set-up. Moreover, scaled rates might exhibit more or less control over the behaviour of the system and therefore, accordingly, incorrect scaling will have more or less influence.ConclusionsWorking with multi-compartment reactions requires a careful consideration of the correct scaling of the rates when changing the volumes of the involved compartments. The error following incorrect scaling - often done by scaling with the volume of the respective compartments can lead to significant aberrations of model behaviour.

Staff views of a hospital at home model implemented in a Scottish care setting.

PurposeDemographic and financial challenges mean prioritising a shift in healthcare provision from acute to community settings. One well-evidenced model encapsulating this is ‘hospital at home’, however limited research has examined staffs' views on its implementation, which may inform service development and increase job satisfaction. The aim within was to explore the staff perspective of implementing a ‘hospital at home’ model in a Scottish care setting which can inform service provision and ultimately increase job satisfaction.MethodsThe ‘Acute Care @ Home’ (AC@H) service had a multi-disciplinary team. Referrals were predominantly received from a geriatric hospital ward. Inclusion criteria were older adults with geriatric syndromes and who required care input for a duration between one to seven days. In-depth staff interviews (N = 13) were conducted and analysed thematically to understand barriers and facilitators to implementation. These were supplemented with questionnaires assessing constructs of interest including training, communication and overall satisfaction.ResultsSeveral themes urged from our study: inter-team and intra-team collaboration, service development and operation, and scaling considerations. High job satisfaction was reported (mean score 73%), particularly due to a perceived non-hierarchical team structure and inclusive management style. Staff attributed positive outcomes through better identifying patients' needs at home compared to in hospital. Continuity of care facilitated rapport building. Recruitment challenges restricted the acuity and volume of patients the team were able to care for.ConclusionsThis qualitative methodology could be useful for future implementation of intermediate care resources for the future health and care system building. Patient assessments at home, as opposed to in hospital, in conjunction with care continuity by staff, may mitigate against hospital risks and better facilitate reablement. Where recruitment challenges are present, agile models of care delivery should be considered.

Chwiejna lojalność żołnierzy Justyniana. Przyczyny rebelii w rzymskiej armii w Afryce w latach 536–546 n.e. Słowa

[The wavering loyalty of Emperor Justinian’s soldiers. The causes of the military revolts in Africa (536–545 AD)] Shortly after the liquidation of the Vandal rule in northern Africa and the restoration of the Roman administration, the newly established prefecture was shaken up by a series of military mutinies and rebellions. The revolts in the years 536–545 AD are represented in the contemporary witness accounts (esp. historian Procopius of Caesarea, poet Flavius Cresconius Corippus) as a case of a “civil war” among the Romans in the context of the concurrent conflict with the Berber (“Moor”) tribes. The history of the army mutinies has been depicted in accordance with the literary conventions and the propaganda‑oriented assumptions of the authors, with a striking background picture of the Roman army in a state of continual readiness to rise up in revolt against the emperor’s authority, which is something virtually unknown from any other contemporaneous war theatre, in consideration of a comparable scale. In his analysis of the unfolding events, the Author attempts to address not only the questions of the political intentions of the various rebellion leaders, but also (or even in particular) the motivations which would make the soldiers take part in such precarious undertakings. He points to a number of political, religious, and economic factors which caused the northern African army mutinies to escalate so violently, concluding with a paradoxical observation that in the newly established prefecture, the emperor would have counted on the loyalty of the locally recruited soldiers rather than on the elite troops to whose military skills he owed the re‑conquering of Africa.

Gamma Boltzmann Machine for Audio Modeling

This paper presents an energy-based probabilistic model that handles nonnegative data in consideration of both linear and logarithmic scales. In audio applications, magnitude of time-frequency representation, including spectrogram, is regarded as one of the most important features. Such magnitude-based features have been extensively utilized in learning-based audio processing. Since a logarithmic scale is important in terms of auditory perception, the features are usually computed with a logarithmic function. That is, a logarithmic function is applied within the computation of features so that a learning machine does not have to explicitly model the logarithmic scale. We think in a different way and propose a restricted Boltzmann machine (RBM) that simultaneously models linear- and log-magnitude spectra. RBM is a stochastic neural network that can discover data representations without supervision. To manage both linear and logarithmic scales, we define an energy function based on both scales. This energy function results in a conditional distribution (of the observable data, given hidden units) that is written as the gamma distribution, and hence the proposed RBM is termed gamma-Bernoulli RBM. The proposed gamma-Bernoulli RBM was compared to the ordinary Gaussian-Bernoulli RBM by speech representation experiments. Both objective and subjective evaluations illustrated the advantage of the proposed model.

Turbine Scale and Siting Considerations in Wind Plant Layout Optimization and Implications for Capacity Density

Turbine Scale and Siting Considerations in Wind Plant Layout Optimization and Implications for Capacity Density

The names don't matter but the numbers do: searching for stability in Carboniferous brachiopod paleocommunities from the North American Midcontinent

AbstractA key question in paleoecology and macroevolution is whether assemblages of species (paleocommunities) are persistent entities that endure over millions of years. While community turnover in the face of abiotic change is the presumed norm, paleocommunities have been shown to persist for long time periods and regardless of environmental disruption. It remains an open question, however, as to what processes allow for this. We investigate these questions by analyzing the Carboniferous brachiopod paleocommunities from the Midcontinent of North America. These diverse communities were subjected to repeated and geologically rapid changes in sea level. Using a suite of statistical techniques, we characterize the nature and scope of changes in these paleocommunities over time. We find that, at the paleocommunity scale, there is no evidence for obdurate ecological stasis, with fluctuations in both taxonomic composition and the associated abundance of taxa. However, at a higher ecological scale, stability is manifest, as diversity patterns remain stable across time, with a consistent number of species that can exist in any given paleocommunity. This suggests ecological rules such as taxon packing are in effect, resulting in a form of ecological stability even in the face of constant disequilibrium, and parallels ecological patterns of disruption and recovery previously observed for invertebrate communities from modern marine systems. Based on these results, we advocate for consideration of different hierarchical entities and scales when interpreting the ecological dynamics of fossil assemblages, as focusing exclusively on changes in taxon identity/abundance or diversity levels can lead to very different results.