How do plant ecologists use matrix population models?

Matrix projection models are among the most widely used tools in plant ecology. However, the way in which plant ecologists use and interpret these models differs from the way in which they are presented in the broader academic literature. In contrast to calls from earlier reviews, most studies of plant populations are based on < 5 matrices and present simple metrics such as deterministic population growth rates. However, plant ecologists also cautioned against literal interpretation of model predictions. Although academic studies have emphasized testing quantitative model predictions, such forecasts are not the way in which plant ecologists find matrix models to be most useful. Improving forecasting ability would necessitate increased model complexity and longer studies. Therefore, in addition to longer term studies with better links to environmental drivers, priorities for research include critically evaluating relative/comparative uses of matrix models and asking how we can use many short-term studies to understand long-term population dynamics.     

生态位模型的理论基础、发展方向与挑战

生态位模型是一个以生态位理论为基础的新兴研究领域.它通过采集研究对象的已知分布点及其相关的环境数据组成训练样本,利用数理统计或机器学习理论分析数据,构建特征函数表示物种在生态位空间的实际生态位.以生态位模型预测物种潜在分布地或计算物种间的生态位重叠等研究,在生态学、生物地理学和进化生物学研究中显得越来越重要.本文从生态位概念出发,详细解析了生态位模型的理论基础、相关的焦点争论、使用时的注意点以及可能的发展方向与面临的挑战,指出模型中要考虑人类活动对物种生态位的影响.希望本文所探讨的本领域最新的争论焦点能引起相关学者的关注与深入思考.     

On pessimism in Australian ecology

In the face of massive human impacts upon the environment, a prevailing attitude among Australian ecologists seems to have become one of pessimism. The essay explores the consequences of this stance. Pessimism arises understandably from personal values favouring care for the natural world under conditions of rapid global change. Expression of such values is in no way illegitimate; pessimism is as justifiable a perspective as is optimism when interpreting ecology. Examples are mentioned of areas of Australian ecology where pessimism does not predominate, and where the ecological changes being wrought by the Anthropocene are accepted. But, the predominance of gloom can cause ecologists to flirt with exaggeration and with misanthropy, and perversely it can cause them to strive to prevent ecological change even though the discipline is rooted in the reality of flux. Most profoundly, it causes many citizens to switch off: gloom has limited capacity to motivate, whereas hope is the elixir of action. Because of predominant negativity, we risk losing the confidence of society. The solution is for the discipline to develop more diverse strands of interpretation, characterized by more equal doses of acceptance and hope. By balancing dejection, ecology may achieve the efficacy in debate and decision‐making that it warrants in these challenging times.     

Poverty,Disease, and the Ecology of Complex Systems

Understanding why some human populations remain persistently poor remains a significant challenge for both the social and natural sciences. The extremely poor are generally reliant on their immediate natural resource base for subsistence and suffer high rates of mortality due to parasitic and infectious diseases. Economists have developed a range of models to explain persistent poverty, often characterized as poverty traps, but these rarely account for complex biophysical processes. In this Essay, we argue that by coupling insights from ecology and economics, we can begin to model and understand the complex dynamics that underlie the generation and maintenance of poverty traps, which can then be used to inform analyses and possible intervention policies. To illustrate the utility of this approach, we present a simple coupled model of infectious diseases and economic growth, where poverty traps emerge from nonlinear relationships determined by the number of pathogens in the system. These nonlinearities are comparable to those often incorporated into poverty trap models in the economics literature, but, importantly, here the mechanism is anchored in core ecological principles. Coupled models of this sort could be usefully developed in many economically important biophysical systems—such as agriculture, fisheries, nutrition, and land use change—to serve as foundations for deeper explorations of how fundamental ecological processes influence structural poverty and economic development.     

Where is behavioural ecology going?

Since the 1990s, behavioural ecologists have largely abandoned some traditional areas of interest, such as optimal foraging, but many long-standing challenges remain. Moreover, the core strengths of behavioural ecology, including the use of simple adaptive models to investigate complex biological phenomena, have now been applied to new puzzles outside behaviour. But this strategy comes at a cost. Replication across studies is rare and there have been few tests of the underlying genetic assumptions of adaptive models. Here, I attempt to identify the key outstanding questions in behavioural ecology and suggest that researchers must make greater use of model organisms and evolutionary genetics in order to make substantial progress on these topics.     

Colour spaces in ecology and evolutionary biology

The recognition that animals sense the world in a different way than we do has unlocked important lines of research in ecology and evolutionary biology. In practice, the subjective study of natural stimuli has been permitted by perceptual spaces, which are graphical models of how stimuli are perceived by a given animal. Because colour vision is arguably the best‐known sensory modality in most animals, a diversity of colour spaces are now available to visual ecologists, ranging from generalist and basic models allowing rough but robust predictions on colour perception, to species‐specific, more complex models giving accurate but context‐dependent predictions. Selecting among these models is most often influenced by historical contingencies that have associated models to specific questions and organisms; however, these associations are not always optimal. The aim of this review is to provide visual ecologists with a critical perspective on how models of colour space are built, how well they perform and where their main limitations are with regard to their most frequent uses in ecology and evolutionary biology. We propose a classification of models based on their complexity, defined as whether and how they model the mechanisms of chromatic adaptation and receptor opponency, the nonlinear association between the stimulus and its perception, and whether or not models have been fitted to experimental data. Then, we review the effect of modelling these mechanisms on predictions of colour detection and discrimination, colour conspicuousness, colour diversity and diversification, and for comparing the perception of colour traits between distinct perceivers. While a few rules emerge (e.g. opponent log–linear models should be preferred when analysing very distinct colours), in general model parameters still have poorly known effects. Colour spaces have nonetheless permitted significant advances in ecology and evolutionary biology, and more progress is expected if ecologists compare results between models and perform behavioural experiments more routinely. Such an approach would further contribute to a better understanding of colour vision and its links to the behavioural ecology of animals. While visual ecology is essentially a transfer of knowledge from visual sciences to evolutionary ecology, we hope that the discipline will benefit both fields more evenly in the future.     

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.     

An invitation to ecological economics

The emerging interdisciplinary field of ecological economics should be a recognized research priority. Only through a combination of sound ecology and good economics can we hope to manage our exploitation of the biosphere in a manner that is both sustainable and efficient. This article is an invitation to ecologists to use economic tools and to participate in ecological economic debate. To this end, we review basic ecological economic concepts and discuss how the field has arisen, what benefits it offers, and what challenges it must overcome.     

School yards and nature trails: ecology education outside the university

Ecologists, conservation biologists and other natural historians increasingly recognize the need to become involved in public education. An ecologically literate public may be the 'last best hope' for a sustainable biosphere. Partnerships between ecologists and educators may be the best hope, though not the last, for moving towards an ecologically literate public. What are some practical schemes for such partnerships? We present a model for ecology education that has shown promise in several South American settings and describe its application in two quite different contexts in southern Argentina. We hope that our colleagues will ponder, criticize, modify, improve and apply the model in settings other than those we present here.     

Stochastic formulation of ecological models and their applications

The increasing use of computer simulation by theoretical ecologists started a move away from models formulated at the population level towards individual-based models. However, many of the models studied at the individual level are not analysed mathematically and remain defined in terms of a computer algorithm. This is not surprising, given that they are intrinsically stochastic and require tools and techniques for their study that may be unfamiliar to ecologists. Here, we argue that the construction of ecological models at the individual level and their subsequent analysis is, in many cases, straightforward and leads to important insights. We discuss recent work that highlights the importance of stochastic effects for parameter ranges and systems where it was previously thought that such effects would be negligible.     

Restoration ecology: the study of applied optimism

As a young trainee in the field of restoration ecology in the modern age, it is difficult to feel optimistic about our future. As many environmental protections are de‐regulated and the climate crisis heightens, I turned to restoration to find hope in a changing world. Restoration ecologists are the optimists of biology. We work every day to make the world a better place and our passion and forward thinking spurred the United Nation's “decade of restoration.” Learning about the successes of the hardworking members of this field gives me hope. As the earth moves toward an unimaginable future, we should continue to try to make the world a better place and encourage those around us to act and restore the environments they value, whether it be large‐scale restoration or preserving garden pollinator habitat. I am forever thankful to restoration ecology and the optimism the field provides.     

Environmental proteomics, biodiversity statistics and food-web structure

Pioneering studies in environmental proteomics have revealed links between protein diversity and ecological function in simple ecological communities, such as microbial biofilms. In the near future, high-throughput proteomic methods will be applied to more complex ecological systems in which microbes and macrobes interact. Data structures in biodiversity and protein surveys have many similarities, so the statistical methods that ecologists use for analyzing biodiversity data should be adapted for use with quantitative surveys of protein diversity. However, increasing quantities of protein and bioinformatics data will not, by themselves, reveal the functional significance of proteins. Instead, ecologists should be measuring changes in the abundance of protein cohorts in response to replicated field manipulations, including nutrient enrichment and removal of top predators.     

Modeling Taxa-Abundance Distributions in Microbial Communities using Environmental Sequence Data

We show that inferring the taxa-abundance distribution of a microbial community from small environmental samples alone is difficult. The difficulty stems from the disparity in scale between the number of genetic sequences that can be characterized and the number of individuals in communities that microbial ecologists aspire to describe. One solution is to calibrate and validate a mathematical model of microbial community assembly using the small samples and use the model to extrapolate to the taxa-abundance distribution for the population that is deemed to constitute a community. We demonstrate this approach by using a simple neutral community assembly model in which random immigrations, births, and deaths determine the relative abundance of taxa in a community. In doing so, we further develop a neutral theory to produce a taxa-abundance distribution for large communities that are typical of microbial communities. In addition, we highlight that the sampling uncertainties conspire to make the immigration rate calibrated on the basis of small samples very much higher than the true immigration rate. This scale dependence of model parameters is not unique to neutral theories; it is a generic problem in ecology that is particularly acute in microbial ecology. We argue that to overcome this, so that microbial ecologists can characterize large microbial communities from small samples, mathematical models that encapsulate sampling effects are required.     

A Striking Profile: Soil Ecological Knowledge in Restoration Management and Science

Available evidence suggests that research in terrestrial restoration ecology has been dominated by the engineering and botanical sciences. Because restoration science is a relatively young discipline in ecology, the theoretical framework for this discipline is under development and new theoretical offerings appear regularly in the literature. In reviewing this literature, we observed an absence of in‐depth discussion of how soils, and in particular the ecology of soils, can be integrated into the developing theory of restoration science. These observations prompted us to assess the current role of soil ecological knowledge in restoration research and restoration practice. Although soils are universally regarded as critical to restoration success, and much research has included manipulations of soil variables, we found that better integration of soil ecological principles could still contribute much to the practice of ecosystem restoration. Here we offer four potential points of departure for increased dialog between restoration ecologists and soil ecologists. We hope to encourage the view that soil is a complex, heterogeneous, and vital entity and that adoption of this point of view can positively affect restoration efforts worldwide.     

Seed germination traits can contribute better to plant community ecology

Analyses of functional traits have become fundamental tools for understanding patterns and processes in plant community ecology. In this context, regenerative seed traits play an important, yet overlooked, role because they largely determine the ability of plants to disperse and re‐establish. A survey of recent publications in community ecology suggests that seed germination traits in particular are neglected at the expense of other relevant but overused traits based only on seed morphology. As a response to this bias, we discuss the functional significance of seed germination traits in comparison with morphological and biophysical seed traits, and advocate their use in vegetation science. We also demonstrate how research in community assembly, climate change and restoration ecology can benefit from the inclusion of germination traits, encompassing functions that cannot be explained solely by adult plant traits. Seed germination experiments conducted in the laboratory or field to quantify these traits provide ecologically meaningful and relatively easy‐to‐obtain information about the functional properties of plant communities. We argue that bridging the gap between seed physiologists and community ecologists will improve the prediction of plant assemblages, and propose further perspectives for including seed traits into the research agenda of functional community ecologists.     

Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses

Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a ‘consumer's guide’ to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient ‘reference’ prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new ‘default’ prior for any Bayesian model.     

Can optimality models and an ‘optimality research program’ help us understand some plant–fungal relationships?

In this paper we suggest that the field of fungal ecology may benefit from the use of optimality models in the context of an ‘optimality research program’ (ORP). An ORP is a research program in the sense of modern philosopher of science Lakatos' [1978. The Methodology of Scientific Research Programmes: philosophical papers, vol. 1. Cambridge University Press, Cambridge] seminal work. An optimality research program has a lengthy history and record of success in the field of behavioural ecology, but has been seldom employed in fungal ecology. We discuss the ORP and provide some examples of how optimality models may be useful in fungal ecology. We suggest that such an approach may benefit experimental fungal ecologists by: providing a framework for organizing knowledge; generating hypotheses; helping in the planning of experiments; aiding in the interpretation of results; and directing the next steps of an experimental research program. We illustrate these benefits by sketching out how an ORP might be used to answer some fundamental questions about the interactions between host plants and arbuscular mycorrhizal fungi.     

To Model or not to Model,That is no Longer the Question for Ecologists

Here, I argue that we should abandon the division between “field ecologists” and “modelers,” and embrace modeling and empirical research as two powerful and often complementary approaches in the toolbox of 21st century ecologists, to be deployed alone or in combination depending on the task at hand. As empirical research has the longer tradition in ecology, and modeling is the more recent addition to the methodological arsenal, I provide both practical and theoretical reasons for integrating modeling more deeply into ecosystem research. Empirical research has epistemological priority over modeling; however, that is, for models to realize their full potential, and for modelers to wield this power wisely, empirical research is of fundamental importance. Combining both methodological approaches or forming “super ties” with colleagues using different methods are promising pathways to creatively exploit the methodological possibilities resulting from increasing computing power. To improve the proficiency of the growing group of model users and ensure future innovation in model development, we need to increase the modeling literacy among ecology students. However, an improved training in modeling must not curtail education in basic ecological principles and field methods, as these skills form the foundation for building and applying models in ecology.