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.     

Integrating the statistical analysis of spatial data in ecology

In many areas of ecology there is an increasing emphasis on spatial relationships. Often ecologists are interested in new ways of analyzing data with the objective of quantifying spatial patterns, and in designing surveys and experiments in light of the recognition that there may be underlying spatial pattern in biotic responses. In doing so, ecologists have adopted a number of widely different techniques and approaches derived from different schools of thought, and from other scientific disciplines. While the adaptation of a diverse array of statistical approaches and methodologies for the analysis of spatial data has yielded considerable insight into various ecological problems, this diversity of approaches has sometimes impeded communication and retarded more rapid progress in this emergent area. Many of these different statistical methods provide similar information about spatial characteristics, but the differences among these methods make it difficult to compare the results of studies that employ contrasting approaches. The papers in this mini-series explore possible areas of agreement and synthesis between a diversity of approaches to spatial analysis in ecology.     

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.     

Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data

With the development of artificial intelligence (AI) technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning. This review focuses on computational approaches and tools for the prediction of protein – DNA/RNA interactions using machine intelligence techniques. We provide an overview of the development progress of computational methods and summarize the advantages and shortcomings of these methods. We further compiled applications in tasks related to the protein – DNA/RNA interactions, and pointed out possible future application trends. Moreover, biological sequence-digitizing representation strategies used in different types of computational methods are also summarized and discussed.     

Teaching and learning in ecology: a horizon scan of emerging challenges and solutions

We currently face significant, anthropogenic, global environmental challenges and the role of ecologists in mitigating these challenges is arguably more important than ever. Consequently there is an urgent need to recruit and train future generations of ecologists, both those whose main area is ecology, but also those involved in the geological, biological and environmental sciences. Here we present the results of a horizon scanning exercise that identified current and future challenges facing the teaching of ecology, through surveys of teachers, students and employers of ecologists. Key challenges identified were grouped in terms of the perspectives of three groups: students, for example the increasing disconnect between people and nature; teachers, for example the challenges associated with teaching the quantitative skills that are inherent to the study of ecology; and society, for example poor societal perceptions of the field of ecology. In addition to the challenges identified, we propose a number of solutions developed at a workshop by a team of ecology teaching experts, with supporting evidence of their potential to address many of the problems raised. These proposed solutions include developing living labs, teaching students to be ecological entrepreneurs and influencers, embedding skills-based learning and coding in the curriculum, an increased role for learned societies in teaching and learning, and using new technology to enhance fieldwork studies including virtual reality, artificial intelligence and real-time spoken language translation. Our findings are focused towards UK higher education, but they should be informative for students and teachers of a wide range of educational levels, policy makers and professional ecologists worldwide.     

A prolegomenon to nonlinear empiricism in the human behavioral sciences

We propose a general framework for integrating theory and empiricism in human evolutionary ecology. We specifically emphasize the joint use of stochastic nonlinear dynamics and information theory. To illustrate critical ideas associated with historical contingency and complex dynamics, we review recent research on social preferences and social learning from behavioral economics. We additionally examine recent work on ecological approaches in history, the modeling of chaotic populations, and statistical application of information theory.     

An evaluation of the state of spatial point pattern analysis in ecology

Over the last two decades spatial point pattern analysis (SPPA) has become increasingly popular in ecological research. To direct future work in this area we review studies using SPPA techniques in ecology and related disciplines. We first summarize the key elements of SPPA in ecology (i.e. data types, summary statistics and their estimation, null models, comparison of data and models, and consideration of heterogeneity); second, we review how ecologists have used these key elements; and finally, we identify practical difficulties that are still commonly encountered and point to new methods that allow current key questions in ecology to be effectively addressed. Our review of 308 articles published over the period 1992–2012 reveals that a standard canon of SPPA techniques in ecology has been largely identified and that most of the earlier technical issues that occupied ecologists, such as edge correction, have been solved. However, the majority of studies underused the methodological potential offered by modern SPPA. More advanced techniques of SPPA offer the potential to address a variety of highly relevant ecological questions. For example, inhomogeneous summary statistics can quantify the impact of heterogeneous environments, mark correlation functions can include trait and phylogenetic information in the analysis of multivariate spatial patterns, and more refined point process models can be used to realistically characterize the structure of a wide range of patterns. Additionally, recent advances in fitting spatially‐explicit simulation models of community dynamics to point pattern summary statistics hold the promise for solving the longstanding problem of linking pattern to process. All these newer developments allow ecologists to keep up with the increasing availability of spatial data sets provided by newer technologies, which allow point patterns and environmental variables to be mapped over large spatial extents at increasingly higher image resolutions.     

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer

BackgroundIn recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design.ObjectiveIn this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches in light of their application to therapeutic response modeling in cancer.ConclusionWe hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.     

Towards a behavioral ecology of ecological landscapes

Recent developments in landscape-level ecological modeling rest upon poorly understood behavioral phenomena. Surprisingly, these phenomena include animal movement and habitat selection, two areas with a long history of study in behavioral ecology. A major problem in applying traditional behavioral ecology to landscape-level ecological problems is that ecologists and behaviorists work at very different spatial scales. Thus a behavioral ecology of ecological landscapes would strive to overcome this inopportune differential in spatial scales. Such a landscape-conscious behavioral undertaking would not only establish more firmly the link between behavior and ecological systems, but also catalyze the study of basic biological phenomena of Interest to behaviorists and ecologists alike.     

Application of multivariate statistical techniques in microbial ecology

Recent advances in high‐throughput methods of molecular analyses have led to an explosion of studies generating large‐scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in‐depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high‐throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure.     

Molecular approaches for AM fungal community ecology: A primer

Molecular techniques are no longer optional for ecologists interested in arbuscular mycorrhizal (AM) communities. Understanding the role of these soil fungi in natural systems requires knowledge of their abundance and identity but this is impossible to achieve without a molecular approach. Adapting molecular tools to AM fungi can be challenging because of the unique biology of the fungi. Moreover, many recruits in the field of mycorrhizal ecology have little or no experience with molecular biology. Here, we outline a conceptual framework for designing robust ecological experiments with AM fungi using molecular approaches.     

Talking Frogs: The Role of Communication in Ecological Research on Private Land

This paper argues that improving the communication between landholders and ecologists will result in better conservation outcomes for ecosystem management on private land. It examines a case study of ecological research on frogs undertaken on private, agricultural land in south-eastern Australia. The paper questions the traditional separation of ecological science from landholders specifically and the public in general. In addressing this issue the authors wish to improve the relevance of ecology for landholders, raise the profile of social science for ecologists working on private land and examine the implications of improving ecologist – landholder relationships. For landholders, an improved understanding of the ecological context of their agricultural activities may lead to sustainability gains. For ecologists, a deeper appreciation for the social context of their ecological research provides an opportunity to see how their work is perceived and/or acted upon in practice. For both parties, a communicative relationship may minimise future need for ecosystem repair. Such an approach (for both landholders and ecologists) can lead to the break down of stereotypes and/or a greater appreciation of the others’ perspectives, constraints and values with respect to conservation on private land. In the productive discussions arising from conversations between landholders and ecologists, new approaches to sustainable land management and nature conservation may emerge.     

Developing a flexible learning activity on biodiversity and spatial scale concepts using open‐access vegetation datasets from the National Ecological Observatory Network

Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.     

Ecological hierarchies and self-organisation – Pattern analysis,modelling and process integration across scales

A continuing discussion in applied and theoretical ecology focuses on the relationship of different organisational levels and on how ecological systems interact across scales. We address principal approaches to cope with complex across-level issues in ecology by applying elements of hierarchy theory and the theory of complex adaptive systems. A top-down approach, often characterised by the use of statistical techniques, can be applied to analyse large-scale dynamics and identify constraints exerted on lower levels. Current developments are illustrated with examples from the analysis of within-community spatial patterns and large-scale vegetation patterns. A bottom-up approach allows one to elucidate how interactions of individuals shape dynamics at higher levels in a self-organisation process; e.g., population development and community composition. This may be facilitated by various modelling tools, which provide the distinction between focal levels and resulting properties. For instance, resilience in grassland communities has been analysed with a cellular automaton approach, and the driving forces in rodent population oscillations have been identified with an agent-based model. Both modelling tools illustrate the principles of analysing higher level processes by representing the interactions of basic components.The focus of most ecological investigations on either top-down or bottom-up approaches may not be appropriate, if strong cross-scale relationships predominate. Here, we propose an ‘across-scale-approach’, closely interweaving the inherent potentials of both approaches. This combination of analytical and synthesising approaches will enable ecologists to establish a more coherent access to cross-level interactions in ecological systems.     

Persistent Controversy in Statistical Approaches in Wildlife Sciences: A Perspective of Students

ABSTRACT The controversy over the use of null hypothesis statistical testing (NHST) has persisted for decades, yet NHST remains the most widely used statistical approach in wildlife sciences and ecology. A disconnect exists between those opposing NHST and many wildlife scientists and ecologists who conduct and publish research. This disconnect causes confusion and frustration on the part of students. We, as students, offer our perspective on how this issue may be addressed. Our objective is to encourage academic institutions and advisors of undergraduate and graduate students to introduce students to various statistical approaches so we can make well-informed decisions on the appropriate use of statistical tools in wildlife and ecological research projects. We propose an academic course that introduces students to various statistical approaches (e.g., Bayesian, frequentist, Fisherian, information theory) to build a foundation for critical thinking in applying statistics. We encourage academic advisors to become familiar with the statistical approaches available to wildlife scientists and ecologists and thus decrease bias towards one approach. Null hypothesis statistical testing is likely to persist as the most common statistical analysis tool in wildlife science until academic institutions and student advisors change their approach and emphasize a wider range of statistical methods.     

Bacteria—A link among ecosystem constituents

Microbial ecology has undergone a revolution over the past two decades due to the numerous innovations in techniques, allowing bacteria to be detected more accurately by direct means. Thus, bacterial life can be distinguishedin situ by direct counting under epifluorescence microscopy; automatically counting and sizing by image analyzer equipped with epifluorescence microscopy or by use of flow cytometry; specific radioisotope techniques; and molecular techniques to detect specific taxa. All of these approaches do not require cultivation, which provides a biased view of bacterial communities in nature. Bacteria are abundant in aquatic environments and play important roles as links between dissolved nutrients and the grazers in the food web. The new techniques allow an evaluation of bacterial population dynamics and function in relation to other organisms of higher trophic levels. This mini review aims to show briefly the state-of-the-art in microbial ecology for ecologists concerned with organisms other than microbes, in order to develop further intensive study together with microbial ecologists.