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At least 649 records · Page 36Linked to original sources
USGS2018

Hydrologic conditions in Kansas, water year 2017

The U.S. Geological Survey, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in Kansas. These data and associated analyses provide a unique overview of the hydrologic conditions and help improve the understanding of Kansas’ water resources. Yearly assessments of hydrologic conditions are made by comparing statistical analysis of current and past water year data for the period of record. These data provide critical information for protecting life and property, managing water supplies, forecasting floods, operating reservoirs, designing bridges and culverts, processing interstate and intrastate water rights claims, ecological monitoring, and many other uses.

Kansas↗
USGS2004

Mute swans: Natural (?) environmental indicators

The rapid expansion of the Chesapeake Bay's population of feral mute swans (Cygnus olar), coupled with a dramatic Bay-wide decline in submerged aquatic vegetation (SAV), has fueled much of the current debate surrounding the need for a management plan to protect the aquatic food resources that are critical to many species native to the Bay. Crucial to this decision process is a sound understanding of the ecological ramifications of having the year-round presence of a large, nonnative, aquatic herbivore on the Bay. Ultimately, this will require a quantitative assessment of the ecological harm currently posed by mute swans before a biologically defensible management strategy can be developed. Unfortunately, very little new information specific to the Bay's mute swan population has been gathered since Reese first studied them in the late 1960s and 1970s. While the debate over what to do about the rapidly expanding mute swan population continues, there is much that can be gained from study of this beautiful intruder. Several recent studies of the feeding habits of mute swans have shown that mutes can provide a unique barometer, or indicator, of environmental conditions. Because of their reliance on SAV as a primary food source, monitoring the density of swans utilizing a particular area can give some indication of the status of the area's grass beds. This phenomenon was clearly demonstrated during the summer of 1999 when there was a dramatic decline in the number of swans observed around the Eastern Neck NWR, a traditional population stronghold. The shift in bird use was precipitated by a rapid, large-scale collapse of the area's aquatic grass beds, possibly the result of a prolonged drought. During the winter of 2000/2001, a similar ecological assessment was conducted by comparing body weights of swans collected from Tangier Sound, an area with relatively abundant grass beds, and swans from the waters adjacent to Eastern Neck Island. Swans weights tended to reflect the conditions of their surroundings, with the Tangier Sound birds being slightly heavier at the onset of the breeding season. Interestingly, the birds at Eastern Neck showed a 1 kg decline in weight after dispersal from their wintering locations and entered the breeding season with noticeably less subcutaneous fat than the birds sampled in Tangier Sound. The fact that mute swans are nonmigratory and feed exclusively on benthic food items makes them an ideal organ-ism to monitor the degree of contamination of sediments within the Bay. In 1995, we compared the accumulation of metals by mute swans and other waterfowl and related it to metal concentrations in the sediments from the areas where the birds were collected. This study led to the development of an exposure model that more accurately assesses the risk of exposure to environmental contaminants through incidental ingestion of sediments, as opposed to the traditional assessment of contaminant accumulation through the food chain. This sediment exposure pathway was subsequently shown to be the primary route of exposure of swans to metals in risk assessments conducted at two Superfund sites.

Book chapter↗
USGS2005

Mismatch between herbivore behavior and demographics contributes to scale-dependence of host susceptibility in two pine species

The impacts on forests of tree-killing bark beetles can depend on the species composition of potential host trees. Host susceptibility might be an intrinsic property of tree species, or it might depend on spatial patterning of alternative host species. We compared the susceptibility of loblolly pine (Pinus taeda) and Virginia pine (P. virginiana) to southern pine beetle (Dendroctonus frontalis) at two hierarchical levels of geographic scale: within beetle infestations in heterospecific stands (extent ranging from 0.28 to 0.65 ha), and across a forest landscape (extent 72,500 ha) that was dominated by monospecific stands. In the former, beetles preferentially attacked Virginia pine (tree mortality = 65-100% in Virginia pine versus 0-66% in loblolly pine), but in the latter, loblolly stands were more susceptible than Virginia stands. This hierarchical transition in host susceptibility was predicted from knowledge of (1) a behavioral preference of beetles for attacking loblolly versus Virginia pine, (2) a negative correlation between preference and performance, and (3) a mismatch in the domain of scale between demographics and host selection by individuals. There is value for forest management in understanding the processes that can produce hierarchical transitions in ecological patterns. Copyright ?? 2005 by the Society of American Foresters.

Forest Science↗
USGS2018

Assessing and communicating the impacts of climate change on the Southern California coast

Over the course of this and the next century, the combination of rising sea levels, severe storms, and coastal erosion will threaten the sustainability of coastal communities, development, and ecosystems as we currently know them. To clearly identify coastal vulnerabilities and develop appropriate adaptation strategies for projected increased levels of coastal flooding and erosion, coastal managers need user-friendly planning tools based on the best available climate and coastal science. In anticipation of these climate change impacts, many communities are in the early stages of climate change adaptation planning but lack the scientific information and tools to adequately address the potential impacts. In collaboration with leading scientists worldwide, the USGS designed the Coastal Storm Modeling System (CoSMoS) to assess the coastal impacts of climate change for the California coast, including the combination of sea level rise, storms, and coastal change. In this project, we directly address the needs of coastal resource managers in Southern California by integrating a vast range of global climate change projections and translate that information using sophisticated physical process models into planning-scale physical, ecological, and economic exposure, shoreline change, and impact assessments, all delivered in two simple, user-friendly, online tools. Our results show that by the end of the 21st century, over 250,000 residents and nearly $40 billion in building value across Southern California could be exposed to coastal flooding from storms, sea level rise, and coastal change. Results for the other major population center in California (the greater San Francisco Bay Area) are also available but not explicitly discussed in this report. Together, CoSMoS has now assessed the exposure of 95% of the 26 million coastal residents of the State (17 million in Southern California).

California↗
USGS2022

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad-extent, fine-grain domain—more accessible via NEON/UAS integration.

Ecosphere↗
USGS2005

Multiple hypotheses testing of fish incidence patterns in an urbanized ecosystem

Ecological and evolutionary theories have focused traditionally on natural processes with little attempt to incorporate anthropogenic influences despite the fact that humans are such an integral part of virtually all ecosystems. A series of alternate models that incorporated anthropogenic factors and traditional ecological mechanisms of invasion to account for fish incidence patterns in urban lakes was tested. The models were based on fish biology, human intervention, and habitat characteristics. However, the only models to account for empirical patterns were those that included fish invasiveness, which incorporated species-specific information about overall tolerance and fecundity. This suggests that species-specific characteristics are more important in general distributional patterns than human-mediated dispersal. Better information of illegal stocking activities is needed to improve human-mediated models, and more insight into basic life history of ubiquitous species is needed to truly understand underlying mechanisms of biotic homogenization. ?? Springer 2005.

Aquatic Ecology↗
USGS2026

Estimation of parental abundance using hierarchical Bayesian modeling with data augmentation

Pedigree-based estimation methods leverage the fact that each offspring in a cohort is genotypically “marked” by its parents and represent a recent and promising toolset for estimating population dynamics. This includes pedigree accumulation estimators that model the “accumulation” of inferred unique parents within a given cohort to estimate parental abundance. Unlike close-kin mark-recapture approaches, which rely on intercohort comparisons, pedigree accumulation modeling can be completed solely using intracohort samples. This is particularly advantageous for semelparous species, where intercohort pairs are impossible and adult life stages can be difficult to sample without affecting their likelihood of successfully reproducing. Previous work has evaluated a range of estimators for such datasets, concluding that the non-parametric Chao estimator provides the most accurate and precise estimates for feasible levels of sampling effort. We used simulated data to evaluate an alternative estimator based on hierarchical modeling and data augmentation in a Bayesian framework. Results indicate that estimates from the hierarchical Bayesian estimator had comparable accuracy and better precision than both the previously tested Chao1 estimator and the improved iChao formulation across a range of sample sizes and sex ratios. Furthermore, the Bayesian estimator was far more robust to simulated errors in pedigree reconstruction, especially the presence of false negatives. Hierarchical Bayesian pedigree accumulation models can also provide additional insight into underlying reproductive ecology through their use of an explicit observation process, allowing for the incorporation or estimation of species- and population-specific reproductive dynamics. More broadly, the parametric nature of these models offers opportunities to efficiently pool information among datasets as well as to propagate uncertainty within more complex models.

Ecology and Evolution↗
USGS2019

Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction

Multivariate compositional count data arise in many applications including ecology, microbiology, genetics and paleoclimate. A frequent question in the analysis of multivariate compositional count data is what underlying values of a covariate(s) give rise to the observed composition. Learning the relationship between covariates and the compositional count allows for inverse prediction of unobserved covariates given compositional count observations. Gaussian processes provide a flexible framework for modeling functional responses with respect to a covariate without assuming a functional form. Many scientific disciplines use Gaussian process approximations to improve prediction and make inference on latent processes and parameters. When prediction is desired on unobserved covariates given realizations of the response variable, this is called inverse prediction. Because inverse prediction is often mathematically and computationally challenging, predicting unobserved covariates often requires fitting models that are different from the hypothesized generative model. We present a novel computational framework that allows for efficient inverse prediction using a Gaussian process approximation to generative models. Our framework enables scientific learning about how the latent processes co-vary with respect to covariates while simultaneously providing predictions of missing covariates. The proposed framework is capable of efficiently exploring the high dimensional, multi-modal latent spaces that arise in the inverse problem. To demonstrate flexibility, we apply our method in a generalized linear model framework to predict latent climate states given multivariate count data. Based on cross-validation, our model has predictive skill competitive with current methods while simultaneously providing formal, statistical inference on the underlying community dynamics of the biological system previously not available.

Annals of Applied Statistics↗
USGS2005

Quantile regression reveals hidden bias and uncertainty in habitat models

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias were induced by confounding with missing variables associated with other improtant processes, a problem common in statistical modeling of ecological phenomena. Simulations for a large ( N = 10 000) finite population representing grid locations on a landscape demonstrated various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable. Quantile (0 ≤ τ ≤ 1) regression parameters for linear models that excluded the important, unmeasured variable revealed bias relative to parameters from the generating model. Depending on whether interactions of the measured and unmeasured variables were negative (interference interactions) or positive (facilitation interactions) in simulations without spatial structuring, either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Heterogeneous, nonlinear response patterns occurred with correlations between the measured and unmeasured variables. When the unmeasured variable was spatially structured, variation in parameters across quantiles associated with heterogeneous effects of the habitat variable was reduced by modeling the spatial trend surface as a cubic polynomial of location coordinates, but substantial hidden bias remained. Sampling ( n = 20–300) simulations demonstrated that regression quantile estimates and confidence intervals constructed by inverting weighted rank score tests provided valid coverage of these parameters. Local forms of quantile weighting were required for obtaining correct Type I error rates and confidence interval coverage. Quantile regression was used to estimate effects of physical habitat resources on a bivalve ( Macomona liliana ) in the spatially structured landscape on a sandflat in a New Zealand harbor. Confidence intervals around predicted 0.10 and 0.90 quantiles were used to estimate sampling intervals containing 80% of the variation in densities in relation to bed elevation. Spatially structured variation in bivalve counts estimated by a cubic polynomial trend surface remained after accounting for the nonlinear effects of bed elevation, indicating the existence of important spatially structured processes that were not adequately represented by the measured habitat variables.

Ecology↗
USGS2013

Maintaining and restoring sustainable ecosystems in southern Nevada

Managers in southern Nevada are challenge with determining appropriate goals and objectives and developing viable approaches for maintaining and restoring sustainable ecosystems in a time of rapid socio-ecological and environmental change. Sustainable or "healthy" ecosystems supply clean air, water and habitat for a diverse array of plants and animals. As described in Chapter 1, sustainable ecosystems retain characteristic processes like hydrological flux and storage, geomorphic processes, biogeochemical cycling and storage, biological activity and productivity, and population regeneration and reproduction over the normal cycle of disturbance events (modified from Chapin and others 1996 and Christensen and others 1996). Ecological restoration of stressed or disturbed ecosystems in an integral part of managing for sustainable ecosystems. The Society of Ecological Restoration International (SERI) defines ecological restoration as the process of assisting the recovery of an ecosystem that has been degraded, damaged, or destroyed (SERI 2004). Many of the southern Nevada's ecosystems are being subjected to anthropogenic stressors that span global, regional, and local scales (Chapter 2)., and are crossing ecological thresholds to new alternative states (Chapter 4 and Chapter 5). These alternative states often represent novel communities with disturbance regimes that differ significantly from historic conditions. Past management and restoration goals often focused on returning ecosystems to pre-disturbance conditions (Harris and others 2006). This approach assumes stable or equilibrium conditions and ignores changes in ecosystems processes due to land uses, increases in CO 2 concentrations, and climate change. A more realistic approach is to base management and restoration goals on the current potential of an ecosystem to support a given set of ecological conditions, and on the likelihood of future change due to warming climate (Harris and others 2006). This approach requires understanding ecosystem resilience to anthropogenic disturbance and climate change, the alternative states that exist for ecosystems, and the factors that result in threshold crossing (Bestelmeyer and others 2009; Hobbs and Harris 2001; Stingham and others 2003; Whisemnant 1999). It also requires the ability to predict how climate is likely to influence ecosystems in the future (Harris and others 2006). This chapter addresses the restoration aspects of Sub-goal 1.3 in the SNAP Science Research Strategy which is to restore and sustain proper function of southern Nevada's watersheds and landscapes (able 1.3; Turner and others 2009). The effects of global, regional and local stresses on southern Nevada ecosystems are presented in Chapter 2. Here, we discuss appropriate objectives and develop guidelines for maintaining and restoring southern Nevada ecosystems. We then discuss the differences in ecological resilience to stress and disturbance and resistance to invasive species in southern Nevada ecosystems and describe restoration and management approaches for the different ecosystem types. We conclude with knowledge gaps and management implications.

Nevada↗
USGS2023

The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology

Species distribution models (SDMs) are widely applied to understand the processes governing spatial and temporal variation in species abundance and distribution but often do not account for measurement errors such as false negatives and false positives. We describe unmarked , a package for the freely available and open-source R software that provides a complete workflow for modelling species distribution and abundance while explicitly accounting for measurement errors. Here we focus on recent advances in unmarked functionality to support multi-species, multi-state, and multi-season data, as well as support for fitting models with random effects. For illustration, we present an analysis of Acadian Flycatcher Empidonax virescens abundance on Roanoke River National Wildlife Refuge, North Carolina, USA, over 18 years. We found that Acadian Flycatcher abundance was initially greater in hardwood plantation habitat relative to bottomland hardwood forest along river levees but that abundance declined over time in both habitats. We plan for unmarked development to keep pace with advances in hierarchical modelling in ecology, including better handling of continuous-time data from camera trap and automated recording units and integrated models for multiple data streams.

Methods in Ecology & Evolution↗
USGS2017

Making ecological models adequate

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems’ responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

Ecology Letters↗
USGS2007

Calculating background levels for ecological risk parameters in toxic harbor sediment

Establishing background levels for biological parameters is necessary in assessing the ecological risks from harbor sediment contaminated with toxic chemicals. For chemicals in sediment, the term contaminated is defined as having concentrations above background and significant human health or ecological risk levels. For biological parameters, a site could be considered contaminated if levels of the parameter are either more or less than the background level, depending on the specific parameter. Biological parameters can include tissue chemical concentrations in ecological receptors, bioassay responses, bioaccumulation levels, and benthic community metrics. Chemical parameters can include sediment concentrations of a variety of potentially toxic chemicals. Indirectly, contaminated harbor sediment can impact shellfish, fish, birds, and marine mammals, and human populations. This paper summarizes the methods used to define background levels for chemical and biological parameters from a survey of ecological risk investigations of marine harbor sediment at California Navy bases. Background levels for regional biological indices used to quantify ecological risks for benthic communities are also described. Generally, background stations are positioned in relatively clean areas exhibiting the same physical and general chemical characteristics as nearby areas with contaminated harbor sediment. The number of background stations and the number of sample replicates per background station depend on the statistical design of the sediment ecological risk investigation, developed through the data quality objective (DQO) process. Biological data from the background stations can be compared to data from a contaminated site by using minimum or maximum background levels or comparative statistics. In Navy ecological risk assessments (ERA's), calculated background levels and appropriate ecological risk screening criteria are used to identify sampling stations and sites with contaminated sediments.

California↗
USGS2024

Feedbacks: A new synthesis of causal loops across ecology

Feedbacks are the basic linkages of living systems. In organisms, they regulate the processes of growth and homeostasis, as well as their interactions with their world. Feedback, which Judson (1980) called ‘one of the chief themes of scientific understanding,' is equally important in ecological systems. The ecological literature is rich in papers dealing with the role of feedback in various phenomena. However, we know of no comprehensive synthesis of feedbacks in ecology. Pichon et al. (2024) accomplish this, and for the first time show that ecological feedbacks can be categorized in terms of a small number of fundamental attributes. The paper brings the array of different types of feedbacks into a manageable order, providing not only the relevant theoretical framework but also guidance on methods for applying understanding to practical issues.

Ecography↗
USGS2024

Upper Mississippi River System hydrogeomorphic change conceptual model and hierarchical classification

Understanding the geomorphic processes and causes for long-term hydrogeomorphic changes along the Upper Mississippi River System (UMRS) is necessary for scientific studies ranging from habitat needs assessments, sediment transport, and nutrient processing, and making sound management decisions and prioritizing ecological restoration activities. From 2018 through 2020 the U.S. Geological Survey and U.S. Army Corps of Engineers led a series of calls and meetings, and a workshop to develop a draft UMRS hydrogeomorphic change conceptual model and hierarchical classification scheme. This project was funded through an Upper Mississippi River Restoration 2018 science in support of restoration proposal entitled, “Conceptual Model and Hierarchical Classification of Hydrogeomorphic Settings in the Upper Mississippi River System.” This report documents the background leading up to and the major findings from the workshop. The resulting conceptual model focuses on the drivers and boundary conditions that affect the major hydrogeomorphic processes along the valley corridor using a continuum of spatial and temporal scales and resolutions. The draft hierarchical classification was based on three existing and three new nested geospatial datasets that ultimately can be used to characterize hydrogeomorphic settings that span the UMRS valley corridor. The conceptual model and hierarchical classification will help characterize recent (mid-1990s through mid-2010s) decadal-scale processes and sources for potential hydrogeomorphic change that span a range of spatial scales from watershed hydrology and sediment sources to channel hydraulics and sediment transport.

Illinois, Indiana, Iowa, Minnesota, Missouri, Sout↗
USGS2022

Partial observability and management of ecological systems

The actual state of ecological systems is rarely known with certainty, but management actions must often be taken regardless of imperfect measurement (partial observability). Because of the difficulties in accounting for partial observability, it is usually treated in an ad hoc fashion, or simply ignored altogether. Yet incorporating partial observability into decision processes lends a realism that has the potential to improve ecological outcomes significantly. We review frameworks for dealing with partial observability, focusing specifically on dynamic ecological systems with Markovian transitions, i.e., transitions among system states that are influenced by the current system state and management action over time. Fully observable states are represented in an observable Markov decision process (MDP), whereas obscure or hidden states are represented in a partially observable process (POMDP). POMDPs can be seen as a natural extension of observable MDPs. Management under partial observability generalizes the situation for complete observability, by recognizing uncertainty about the system's state and incorporating sequential observations associated with, but not the same as, the states themselves. Decisions that otherwise would depend on the actual state must be based instead on state probability distributions (“belief states”). Partial observability requires adaptation of the entire decision process, including the use of belief states and Bayesian updates, valuation that includes expectations over observations, and optimal strategy that identifies actions for belief states over a continuous belief space. We compare MDPs and POMDPs and highlight POMDP applications to some common ecological problems. We clarify the structure and operations, approaches for finding solutions, and analytic challenges of POMDPs for practicing ecologists. Both observable and partially observable MDPs can use an inductive approach to identify optimal strategies and values, with a considerable increase in mathematical complexity with POMDPs. Better understanding of POMDPs can help decision makers manage imperfectly measured ecological systems more effectively.

Ecology and Evolution↗
USGS2025

Adaptive management for recreational fisheries decisions in the face of uncertainty

Recreational fisheries management requires making decisions that consider social, economic, and ecological dimensions. The feedbacks among ecological changes and social responses by fishers create complexity and uncertainty. However, uncertainty can impede the integration of ecological and social dimensions, particularly when considering human behavioural responses to management decisions and ecological changes. One of the few ways to reduce these uncertainties is via experimentation at the system level. Adaptive management (and the larger umbrella of decision analysis) provides a framework to implement purposeful management experiments in a structured manner to learn through an iterative decision process, thereby allowing for the reduction in social and ecological uncertainties in response to planned policy interventions. We discuss the theory of adaptive management and conditions for which it is appropriate, as well as the benefits and costs of applying active versus passive adaptive management to reduce social and ecological uncertainties for recreational fisheries. We then provide key examples from previous studies on decisions for zoning in the Great Barrier Reef, Australia, stocking decisions that include stakeholder learning in Germany, and decisions for stocking and harvest management in British Columbia, that demonstrate best practices for implementation of adaptive management to reduce uncertainties surrounding human behaviour and other social and ecological objectives related to recreational fisheries.

Book chapter↗
USGS2025

Best practices for understanding recreational fishers

In this closing chapter of our edited book, we summarize what we believe are best practices for understanding recreational fishers. Fishers are an integral part of the recreational fishery social-ecological system, and we emphasize the importance of placing them in that context. We begin with an overview of the process of developing a project and conclude with some broad suggestions for standardising approaches to gather data from and about fishers, mainly focusing on social science methods. Throughout, we emphasize tactics to promote the development of inter- and transdisciplinary tools and processes, as a means of more fully understanding the full social-ecological system. This chapter draws from methodological details and suggestions developed throughout this book; we describe how they work together but point the reader to these specific chapters to further understand how to build interdisciplinarity into research and management.

Book chapter↗