Catalogue Search | MBRL
هل أنت متأكد أنك تريد إزالة الكتاب من الرف؟
{{itemTitle}}
1,349,833
نتائج ل
"Ecology."
صنف حسب:
50 things you should know about the environment
From the icy poles to the evergreen rainforests, life has found a way to flourish in nearly every environment on Earth. This book explores the startling discoveries of new life forms in extreme environments, such as the strange worlds of the ocean depths. Whilst marveling the world around us, it also confronts the human impact on the environment. The mounting evidence for global warming is explored and challenges us to work towards a more sustainable future. Packed with facts, diagrams, infographics and photos, this is the perfect introduction to the wonder and intrigue of our environment.
Book
Registered Reports: A new chapter at Ecology & Evolution
Ecology & Evolution has published its first Registered Report and offers the perspective of the editor, author, and student on the publication process.
Journal Article
You are never alone
\"[This] picture book ... explores how humans are inextricably connected to nature\"--Provided by publisher.
Book
How much does the typical ecological meta‐analysis overestimate the true mean effect size?
Many primary research studies in ecology are underpowered, providing very imprecise estimates of effect size. Meta‐analyses partially mitigate this imprecision by combining data from different studies. But meta‐analytic estimates of mean effect size may still remain imprecise, particularly if the meta‐analysis includes a small number of studies. Imprecise, large‐magnitude estimates of mean effect size from small meta‐analyses likely would shrink if additional studies were conducted (regression towards the mean). Here, I propose a way to estimate and correct this regression to the mean, using meta‐meta‐analysis (meta‐analysis of meta‐analyses). Hierarchical random effects meta‐meta‐analysis shrinks estimated mean effect sizes from different meta‐analyses towards the grand mean, bringing those estimated means closer on average to their unknown true values. The intuition is that, if a meta‐analysis reports a mean effect size much larger in magnitude than that reported by other meta‐analyses, that large mean effect size likely is an overestimate. This intuition holds even if different meta‐analyses of different topics have different true mean effect sizes. Drawing on a compilation of data from hundreds of ecological meta‐analyses, I find that the typical (median) ecological meta‐analysis overestimates the absolute magnitude of the true mean effect size by ~10%. Some small ecological meta‐analyses overestimate the magnitude of the true mean effect size by >50%. Meta‐meta‐analysis is a promising tool for improving the accuracy of meta‐analytic estimates of mean effect size, particularly estimates based on just a few studies.
Meta‐analytic estimates of mean effect size can be imprecise and overestimate effect magnitude, particularly if the meta‐analysis includes few studies. Here, I use meta‐meta‐analysis (meta‐analysis of meta‐analyses) to quantify and correct for overestimation of the magnitude of mean effect sizes in ecological meta‐analyses. The typical (median) ecological meta‐analysis overestimates the magnitude of the mean effect size by ~10%, and some meta‐analyses overestimate the magnitude of the mean effect size by >50%.
Journal Article
You are stardust
Introduces readers to the extensive and surprising ways in which they're connected to the natural world around them.
Book
VertNet: a new model for biodiversity data sharing
Responding to the urgent need to make biodiversity records broadly accessible, the natural history community turned to “the cloud.”
Journal Article
Coastal habitats
Introduces coastal habitats, covering such topics as landforms, waves, currents, estuaries, coral reefs, plants and animals, and the threats posed by pollution, overfishing, and global warming.
Book
Identification of 100 fundamental ecological questions
1. Fundamental ecological research is both intrinsically interesting and provides the basic knowledge required to answer applied questions of importance to the management of the natural world. The 100th anniversary of the British Ecological Society in 2013 is an opportune moment to reflect on the current status of ecology as a science and look forward to high-light priorities for future work. 2. To do this, we identified 100 important questions of fundamental importance in pure ecology. We elicited questions from ecologists working across a wide range of systems and disciplines. The 754 questions submitted (listed in the online appendix) from 388 participants were narrowed down to the final 100 through a process of discussion, rewording and repeated rounds of voting. This was done during a two-day workshop and thereafter. 3. The questions reflect many of the important current conceptual and technical pre-occupations of ecology. For example, many questions concerned the dynamics of environmental change and complex ecosystem interactions, as well as the interaction between ecology and evolution. 4. The questions reveal a dynamic science with novel subfields emerging. For example, a group of questions was dedicated to disease and micro-organisms and another on human impacts and global change reflecting the emergence of new subdisciplines that would not have been foreseen a few decades ago. 5. The list also contained a number of questions that have perplexed ecologists for decades and are still seen as crucial to answer, such as the link between population dynamics and life-history evolution. 6. Synthesis. These 100 questions identified reflect the state of ecology today. Using them as an agenda for further research would lead to a substantial enhancement in understanding of the discipline, with practical relevance for the conservation of biodiversity and ecosystem function.
Journal Article
Forest habitats
Introduces forest habitats, covering such topics as the history of forests from 354 million years ago, the different types of forests, and the current deforestation due to population growth and agricultural and urban development.
Book
A user‐friendly guide to using distance measures to compare time series in ecology
Abstract Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g., assigning species to individual bird calls); clustering (e.g., clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g., accuracy of model predictions to original time series data); and anomaly detection (e.g., detecting possible catastrophic events from population time series). These common tasks in ecological research all rely on the notion of (dis‐) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time‐series‐related tasks. Therefore, many potential applications remain unexplored. Here, we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real‐world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real‐world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well suited to comparing noisy time series and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit‐for‐purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series and help us answer new ecological questions.
Journal Article