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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%.
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
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.
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.
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.
Camera trapping has consequently spread across the global south and developing countries (Agha et al., 2018; Cremonesi et al., 2021; Galindo-Aguilar et al., 2022). Many private citizens run their own ...camera traps; networking observations from these citizen scientists have yielded great insights and will continue to do so (McShea et al., 2016). ...though camera trapping has largely been used for mammals, it is expanding taxonomically to include vegetation communities (Seyednasrollah et al., 2019; Sun et al., 2021), herptiles (Moore et al., 2020; Welbourne et al., 2020), and avifauna (Jachowski et al., 2015; Murphy et al., 2018).
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.
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