Hasil untuk "Ecology"

T. Battin, K. Besemer, M. Bengtsson et al.

M. Barnes, C. Turner

Environmental DNA (eDNA) refers to the genetic material that can be extracted from bulk environmental samples such as soil, water, and even air. The rapidly expanding study of eDNA has generated unprecedented ability to detect species and conduct genetic analyses for conservation, management, and research, particularly in scenarios where collection of whole organisms is impractical or impossible. While the number of studies demonstrating successful eDNA detection has increased rapidly in recent years, less research has explored the “ecology” of eDNA—myriad interactions between extraorganismal genetic material and its environment—and its influence on eDNA detection, quantification, analysis, and application to conservation and research. Here, we outline a framework for understanding the ecology of eDNA, including the origin, state, transport, and fate of extraorganismal genetic material. Using this framework, we review and synthesize the findings of eDNA studies from diverse environments, taxa, and fields of study to highlight important concepts and knowledge gaps in eDNA study and application. Additionally, we identify frontiers of conservation-focused eDNA application where we see the most potential for growth, including the use of eDNA for estimating population size, population genetic and genomic analyses via eDNA, inclusion of other indicator biomolecules such as environmental RNA or proteins, automated sample collection and analysis, and consideration of an expanded array of creative environmental samples. We discuss how a more complete understanding of the ecology of eDNA is integral to advancing these frontiers and maximizing the potential of future eDNA applications in conservation and research.

D. Bolnick, P. Amarasekare, M. S. Araújo et al.

A. Ho, D. Lonardo, P. Bodelier

W. Larcher

Z. Naveh, Arthur S. Lieberman

J. Krebs, N. Davies

W. Bond, J. Keeley

R. Forman

J. Wiens, C. Graham

B. Fry

M. Begon, J. Harper, C. Townsend

F. Berkes

This book deals with the topic of traditional ecological knowledge specifically in the context of natural resource management. An issue of today is how humans can develop a more acceptable relationship with the environment that supports them. Growing interest in traditional ecological knowledge is perhaps indicative of two things: the need for ecological insights from indigenous practices of resource use; and the need to develop a new ecological ethic in part by learning from the wisdom of traditional knowledge holders. This book explores both of these ideas together by treating traditional ecological knowledge as a knowledge-practice-belief complex. This complex looks at traditional knowledge at four interrelated levels: local knowledge (species specific); the resource management system; social institutions; and worldview (religion, ethics, and defined belief systems). Divided into three parts that deal with concepts, practices and issues, respectively, the book examines many traditional knowledge systems. It discusses the usefulness of traditional ecological knowledge in terms of providing an understanding, not merely information, which is complementary to scientific ecology. At the same time, the book explores a diversity of relationships that different groups have developed with their environment, using extensive case studies.

J. Ludwig, J. Reynolds

H. Neufeld

G. Muyzer, K. Smalla

Christopher A. Lepczyk, Myla F. J. Aronson, K. Evans et al.

A. Hastings, Karen C. Abbott, K. Cuddington et al.

Making sense of transient dynamics Ecological systems can switch between alternative dynamic states. For example, the species composition of the community can change or nutrient dynamics can shift, even if there is little or no change in underlying environmental conditions. Such switches can be abrupt or more gradual, and a growing number of studies examine the transient dynamics between one state and another—particularly in the context of anthropogenic global change. Hastings et al. review current knowledge of transient dynamics, showing that hitherto idiosyncratic and individual patterns can be classified into a coherent framework, with important general lessons and directions for future study. Science, this issue p. eaat6412 BACKGROUND Much of ecological theory and the understanding of ecological systems has been based on the idea that the observed states and dynamics of ecological systems can be represented by stable asymptotic behavior of models describing these systems. Beginning with early work by Lotka and Volterra through the seminal work of May in the 1970s, this view has dominated much of ecological thinking, although concepts such as the idea of tipping points in ecological systems have played an increasingly important role. In contrast to the implied long time scales of asymptotic behavior in mathematical models, both observations of ecological systems and questions related to the management of ecological systems are typically focused on relatively short time scales. A number of models and observations demonstrate possible transient behavior that may persist over very long time periods, followed by rapid changes in dynamics. In these examples, focusing solely on the long-term behavior of systems would be misleading. A long transient is a persistent dynamical regime—including near-constant dynamics, cyclic dynamics, or even apparently chaotic dynamics—that persists for more than a few and as many as tens of generations, but which is not the stable long-term dynamic that would eventually occur. These examples have demonstrated the potential importance of transients but have often appeared to be a set of idiosyncratic cases. What is needed is an organized approach that describes when a transient behavior is likely to appear, predicts what factors enhance long transients, and describes the characteristics of this transient behavior. A theory of long ecological transients is a counterpart to the related question of tipping points, where previous work based on an analysis of simple bifurcations has provided broad insights. ADVANCES Just as ideas based on the saddle-node bifurcation provide a basis for understanding tipping points, a suite of ideas from dynamical systems provides a way to organize a systematic study of transient dynamics in ecological systems. As illustrated in the figure, a relatively small number of ideas from dynamical systems are used to categorize the different ways that transients can arise. Translating these abstract results from dynamical systems into observations about both ecological models and ecological system dynamics, it is possible to understand when transients are likely to occur and the various properties of these transients, with implications for ecosystem management and basic ecological theory. Transients can provide an explanation for observed regime shifts that does not depend on underlying environmental changes. Systems that continually change rapidly between different long-lasting dynamics, such as insect outbreaks, may most usefully be viewed using the framework of long transients. An initial focus on conceptual systems, such as two-species systems, establishes the ubiquity of transients and an understanding of what ecological aspects can lead to transients, including the presence of multiple time scales and particular nonlinear interactions. The influences of stochasticity and more realistic higher-dimensional dynamics are shown to increase the likelihood, and possibly the temporal extent, of transient dynamics. OUTLOOK The development of such a framework for organizing the study of transients in ecological systems opens up a number of avenues for future research and application. The approach we describe also raises important questions for further development in dynamical systems. We have not, for example, emphasized nonautonomous systems, which may be required to understand the implications of a changing environment for transients. Systems with explicit time dependence as well as stochastic nonlinear systems still present great mathematical challenges. Implications for management and basic ecological understanding depend on both the results we describe and future developments. A recognition of the difficulty of prediction caused by long transients, and of the corresponding need to match dynamics to transient behaviors of models, shows that basing either management or interpretation of ecological observations only on long-term dynamics can be seriously flawed. Two ways that long transients arise in ecology, illustrated as a ball rolling downhill. (A) Slow transition away from a ghost attractor: a state that is not an equilibrium, but would be under slightly different conditions. (B) Lingering near a saddle: a state that is attracting from some directions but repelling from others. Additional factors such as stochasticity, multiple time scales, and high system dimension can extend transients. The importance of transient dynamics in ecological systems and in the models that describe them has become increasingly recognized. However, previous work has typically treated each instance of these dynamics separately. We review both empirical examples and model systems, and outline a classification of transient dynamics based on ideas and concepts from dynamical systems theory. This classification provides ways to understand the likelihood of transients for particular systems, and to guide investigations to determine the timing of sudden switches in dynamics and other characteristics of transients. Implications for both management and underlying ecological theories emerge.

Sylvain Christin, É. Hervet, N. Lecomte

A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.

J. Catford, John R. U. Wilson, P. Pyšek et al.

Context dependence is widely invoked to explain disparate results in ecology. It arises when the magnitude or sign of a relationship varies due to the conditions under which it is observed. Such variation, especially when unexplained, can lead to spurious or seemingly contradictory conclusions, which can limit understanding and our ability to transfer findings across studies, space, and time. Using examples from biological invasions, we identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies. Addressing context dependence is a critical challenge in ecology, essential for increased understanding and prediction.