Ecology & Evolution has published its first Registered Report and offers the perspective of the editor, author, and student on the publication process.
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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%.
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Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers ...everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics. Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R Step-by-step instructions for fitting models to messy, real-world data Balanced view of different statistical approaches Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling) Techniques for data manipulation and graphical display Companion Web site with data and R code for all examples
Managing populations of wild harvested species requires the ability to regularly provide accurate abundance assessments. For most marine species, changes in abundance can only be monitored ...indirectly, using methods reliant on harvest-based indices, with significant inherent limitations surrounding the estimation and standardization of harvest effort. Tropical tunas are some of the most exploited marine species in the world and are among several species in critical need of alternative methods for estimating abundance. Addressing this concern, we developed the Associative Behaviour-Based abundance Index (ABBI), designed to provide direct abundance estimates for animals, which exhibit an associative behaviour with aggregation sites. Its implementation in the western Indian Ocean on skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus obesus), revealed similar trajectories in their relative abundance. The ABBI stands as a potentially promising alternative to enhance traditional tropical tuna stock assessments methods, as well as a new opportunity to assess the abundance of other wild species that display an associative behaviour with physical structures found in their natural environment.Managing populations of wild harvested species requires the ability to regularly provide accurate abundance assessments. For most marine species, changes in abundance can only be monitored indirectly, using methods reliant on harvest-based indices, with significant inherent limitations surrounding the estimation and standardization of harvest effort. Tropical tunas are some of the most exploited marine species in the world and are among several species in critical need of alternative methods for estimating abundance. Addressing this concern, we developed the Associative Behaviour-Based abundance Index (ABBI), designed to provide direct abundance estimates for animals, which exhibit an associative behaviour with aggregation sites. Its implementation in the western Indian Ocean on skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus obesus), revealed similar trajectories in their relative abundance. The ABBI stands as a potentially promising alternative to enhance traditional tropical tuna stock assessments methods, as well as a new opportunity to assess the abundance of other wild species that display an associative behaviour with physical structures found in their natural environment.
In the Atlantic Arctic, bowhead whales (Balaena mysticetus) were nearly exterminated by European whalers between the seventeenth and nineteenth centuries. The collapse of the East ...Greenland-Svalbard-Barents Sea population, from an estimated 50 000 to a few hundred individuals, drastically reduced predation on mesozooplankton. Here, we tested the hypothesis that this event strongly favoured the demography of the little auk (Alle alle), a zooplanktivorous feeder competitor of bowhead whales and the most abundant seabird in the Arctic. To estimate the effect of bowhead whaling on little auk abundance, we modelled the trophic niche overlap between the two species using deterministic simulations of mesozooplankton spatial distribution. We estimated that bowhead whaling could have led to a 70% increase in northeast Atlantic Arctic little auk populations, from 2.8 to 4.8 million breeding pairs. While corresponding to a major population increase, this is far less than predicted by previous studies. Our study illustrates how a trophic shift can result from the near extirpation of a marine megafauna species, and the methodological framework we developed opens up new opportunities for marine trophic modelling.In the Atlantic Arctic, bowhead whales (Balaena mysticetus) were nearly exterminated by European whalers between the seventeenth and nineteenth centuries. The collapse of the East Greenland-Svalbard-Barents Sea population, from an estimated 50 000 to a few hundred individuals, drastically reduced predation on mesozooplankton. Here, we tested the hypothesis that this event strongly favoured the demography of the little auk (Alle alle), a zooplanktivorous feeder competitor of bowhead whales and the most abundant seabird in the Arctic. To estimate the effect of bowhead whaling on little auk abundance, we modelled the trophic niche overlap between the two species using deterministic simulations of mesozooplankton spatial distribution. We estimated that bowhead whaling could have led to a 70% increase in northeast Atlantic Arctic little auk populations, from 2.8 to 4.8 million breeding pairs. While corresponding to a major population increase, this is far less than predicted by previous studies. Our study illustrates how a trophic shift can result from the near extirpation of a marine megafauna species, and the methodological framework we developed opens up new opportunities for marine trophic modelling.
A number of factors have recently caused mass coral mortality events in all of the world's tropical oceans. However, little is known about the timing, rate or spatial variability of the loss of ...reef-building corals, especially in the Indo-Pacific, which contains 75% of the world's coral reefs.
We compiled and analyzed a coral cover database of 6001 quantitative surveys of 2667 Indo-Pacific coral reefs performed between 1968 and 2004. Surveys conducted during 2003 indicated that coral cover averaged only 22.1% (95% CI: 20.7, 23.4) and just 7 of 390 reefs surveyed that year had coral cover >60%. Estimated yearly coral cover loss based on annually pooled survey data was approximately 1% over the last twenty years and 2% between 1997 and 2003 (or 3,168 km(2) per year). The annual loss based on repeated measures regression analysis of a subset of reefs that were monitored for multiple years from 1997 to 2004 was 0.72 % (n = 476 reefs, 95% CI: 0.36, 1.08).
The rate and extent of coral loss in the Indo-Pacific are greater than expected. Coral cover was also surprisingly uniform among subregions and declined decades earlier than previously assumed, even on some of the Pacific's most intensely managed reefs. These results have significant implications for policy makers and resource managers as they search for successful models to reverse coral loss.
Destruction of habitat is the major cause for loss of biodiversity. This volume presents the population ecology of Atlantic salmon and brown trout and how it is influenced by the environment in terms ...of growth, migration, spawning and recruitment.
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9.
Numerical Ecology with R Borcard, Daniel; Gillet, Francois; Legendre, Pierre
2011, 20101229, 2011-03-01
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Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the ...authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site (http://www.bio.umontreal.ca/numecolR/). This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment.
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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.
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