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.     

The scope of mission-orientation in Dutch fresh-water ecology

In response to growing concern about environmental problems ecologists have engaged in a variety of mission-oriented efforts in which they claim to have taken into account the objective of helping to solve environmental problems in their research strategies or research programmes. The significance of these efforts is evaluated here in terms of both the theoretical development of the field of ecology and its orientation towards social objectives.Three examples of mission-orientation are analyzed on the basis of a case-study of Dutch fresh-water ecology; (1) ecosystems research within the framework of the International Biological Programme; (2) landscape ecology, and (3) ecological research on the management of fresh-water resources. These examples demonstrate that in principle the scope of missionorientation in ecology can be broad. In Dutch fresh-water ecology, however, two specific approaches have become particularly institutionalized. The ecologists tended to opt either for theory-centered approaches close to the type of research carried out by ecologists developing the field regardless of any societal mission or for problem-centered approaches without much emphasis on theory development. Types of mission-orientation which can be placed between these extremes have been established only to a limited extent in Dutch fresh-water ecology.     

Crosstalk between plant responses to pathogens and herbivores: a view from the outside in

Plants encounter numerous pests and pathogens in the natural environment. An appropriate response to attack by such organisms can lead to tolerance or resistance mechanisms that enable the plant to survive. Many studies concentrate on the signalling pathways that enable plants to recognize and respond to attack, and measure the downstream effect in either biochemical or molecular terms. At the whole plant level, ecologists examine the fitness costs of attack not only for the plant but also over a range of trophic levels. The links between these differing levels of study are beginning to be addressed by the adoption of molecular approaches in more ecologically relevant settings. This review will describe the different approaches used by ecologists and cell biologists in this field and will try to address the question of how we can explore the response to, and consequences, of attack by multiple enemies.     

Machine learning methods without tears: a primer for ecologists

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.     

Conducting robust ecological analyses with climate data

Although the number of studies discerning the impact of climate change on ecological systems continues to increase, there has been relatively little sharing of the lessons learnt when accumulating this evidence. At a recent workshop entitled ‘Using climate data in ecological research’ held at the UK Met Office, ecologists and climate scientists came together to discuss the robust analysis of climate data in ecology. The discussions identified three common pitfalls encountered by ecologists: 1) selection of inappropriate spatial resolutions for analysis; 2) improper use of publically available data or code; and 3) insufficient representation of the uncertainties behind the adopted approach. Here, we discuss how these pitfalls can be avoided, before suggesting ways that both ecology and climate science can move forward. Our main recommendation is that ecologists and climate scientists collaborate more closely, on grant proposals and scientific publications, and informally through online media and workshops. More sharing of data and code (e.g. via online repositories), lessons and guidance would help to reconcile differing approaches to the robust handling of data. We call on ecologists to think critically about which aspects of the climate are relevant to their study system, and to acknowledge and actively explore uncertainty in all types of climate data. And we call on climate scientists to make simple estimates of uncertainty available to the wider research community. Through steps such as these, we will improve our ability to robustly attribute observed ecological changes to climate or other factors, while providing the sort of influential, comprehensive analyses that efforts to mitigate and adapt to climate change so urgently require.     

Footprints on water: the genetic wake of dispersal among reefs

Analysis of genetic data can reveal past and ongoing demographic connections between reef populations. The history, extent, and geography of isolation and exchange help to determine which populations are evolutionarily distinct and how to manage threatened reefs. Here the genetic approaches undertaken to understand connectivity among reefs are reviewed, ranging from early allozyme studies on genetic subdivision, through the use of sequence data to infer population histories, to emerging analyses that pull the influences of the past connections away from the effects of ongoing dispersal. Critically, some of these new approaches can infer migration and isolation over recent generations, thus offering the opportunity to answer many questions about reef connectivity and to better collaborate with ecologists and oceanographers to address problems that remain.     

The experimental paradigm and long-term population studies

Most ecologists recognize the value of long-term studies to population and community ecology, and many also subscribe to the experimental approach as the most effective way of obtaining ecological knowledge. But if we are experimentalists, do we need long-term studies? I argue that the answer to this question is yes, that we must combine these two approaches to solve the major ecological questions of the next century. Most of the challenging questions facing ecologists involve systems subject to long-term time trends or high environmental variability.
Because of the statistical power of many ecological methods, long-term studies are essential to measure time trends in ecosystems. Ignoring statistical power has been a major problem with short-term studies, which have predominated in the ecological literature.
Some examples of long-term studies on larch bud-moth Zeiraphera diniana , winter moth Operophthera brumata and snowshoe hares are discussed briefly to illustrate the four major desiderata of long-term projects: spatial scale, sampling design, hypothesis testing and timeframe.
Two reasons for not doing long-term studies are to assess density-dependence and to monitor ecosystem health. The density-dependent paradigm is bankrupt and has produced much argument and little understanding of population processes. Monitoring of populations is politically attractive but ecologically banal unless it is coupled with experimental work to understand the mechanisms behind system changes.     

Ecological and conservation insights from reconstructive studies of temperate old-growth forests

Reconstructive studies that use paleoecological, dendroecological, historical and other approaches in order to interpret long-term ecosystem dynamics are increasingly generating valuable insights for ecologists, conservationists and foresters who are interested in the ecology, protection and management of old-growth forests. In most cases, the historical context provided by these studies reveals a long-term pattern of change that challenges assumptions about the pristine condition of these systems. Ironically, it is the history of environmental fluctuations, natural disturbance processes, and subtle, often indirect, human impacts that is revealed by reconstructive work that may shape the characteristic structure, composition and ecosystem processes of old-growth forests, and that will certainly provide the greatest challenge to their future conservation and management.     

New approaches to analyzing microbial biodiversity data

Modern molecular techniques have revealed an extraordinary diversity of microorganisms, most of which are as yet uncharacterized. This poses a major challenge to microbial ecologists: how can one compare the microbial diversity of different environments when the vast majority of microbial taxa are usually unknown? Three statistical approaches developed by ecologists and evolutionary biologists--parametric estimation, nonparametric estimation and community phylogenetics--are proving to be promising tools to meet this challenge. The combination of these tools with molecular biology techniques allow the rigorous estimation and comparison of microbial diversity in different environments.     

Facing the Heat: Does Desiccation and Thermal Stress Explain Patterns of Orientation in an Intertidal Invertebrate?

A key challenge for ecologists is to quantify, explain and predict the ecology and behaviour of animals from knowledge of their basic physiology. Compared to our knowledge of many other types of distribution and behaviour, and how these are linked to individual function, we have a poor level of understanding of the causal basis for orientation behaviours. Most explanations for patterns of animal orientation assume that animals will modify their exposure to environmental factors by altering their orientation. We used a keystone grazer on rocky shores, the limpet Cellana tramoserica, to test this idea. Manipulative experiments were done to evaluate whether orientation during emersion affected limpet desiccation or body temperature. Body temperature was determined from infrared thermography, a technique that minimises disturbance to the test organism. No causal relationships were found between orientation and (i) level of desiccation and (ii) their body temperature. These results add to the growing knowledge that responses to desiccation and thermal stress may be less important in modifying the behaviour of intertidal organisms than previously supposed and that thermoregulation does not always reflect patterns of animal orientation. Much of what we understand about orientation comes from studies of animals able to modify orientation over very short time scales. Our data suggests that for animals whose location is less flexible, orientation decisions may have less to do with responses to environmental factors and more to do with structural habitat properties or intrinsic individual attributes. Therefore we suggest future studies into processes affecting orientation must include organisms with differing levels of behavioural plasticity.     

Advancing ecological understandings through technological transformations in noninvasive genetics

Noninvasive genetic approaches continue to improve studies in molecular ecology, conservation genetics and related disciplines such as forensics and epidemiology. Noninvasive sampling allows genetic studies without disturbing or even seeing the target individuals. Although noninvasive genetic sampling has been used for wildlife studies since the 1990s, technological advances continue to make noninvasive approaches among the most used and rapidly advancing areas in genetics. Here, we review recent advances in noninvasive genetics and how they allow us to address important research and management questions thanks to improved techniques for DNA extraction, preservation, amplification and data analysis. We show that many advances come from the fields of forensics, human health and domestic animal health science, and suggest that molecular ecologists explore literature from these fields. Finally, we discuss how the combination of advances in each step of a noninvasive genetics study, along with fruitful areas for future research, will continually increase the power and role of noninvasive genetics in molecular ecology and conservation genetics.     

The growing contributions of molecular biology and immunology to protistan ecology: molecular signatures as ecological tools

Modern genetic and immunological techniques have become important tools for assessing protistan species diversity for both the identification and quantification of specific taxa in natural microbial communities. Although these methods are still gaining use among ecologists, the new approaches have already had a significant impact on our understanding of protistan diversity and biogeography. For example, genetic studies of environmental samples have uncovered many protistan phylotypes that do not match the DNA sequences of any cultured organisms, and whose morphological identities are unknown at the present time. Additionally, rapid and sensitive methods for detecting and enumerating taxa of special importance (e.g. bloom-forming algae, parasitic protists) have enabled much more detailed distributional and experimental studies than have been possible using traditional methods. Nevertheless, while the application of molecular approaches has advanced some aspects of aquatic protistan ecology, significant issues still thwart the widespread adoption of these approaches. These issues include the highly technical nature of some of the molecular methods, the reconciliation of morphology-based and sequence-based species identifications, and the species concept itself.     

Problems of kin recognition

Behavioural ecologists have long assumed that animals discriminate between their kin and non-kin, but paid little attention to how animals recognize their relatives. Although the first papers on kin recognition mechanisms appeared barely 10 years ago, studies now appear frequently in journals of animal behaviour. Initial findings reveal that kin recognition abilities are surprisingly well-distributed throughout the animal kingdom. Yet an understanding of the evolutionary and ecological significance of these abilities demands further analyses of the components of kin recognition mechanisms and the social contexts in which they are expressed. Many controversies and unresolved issues remain, and experimental approaches to these problems promise to continue making kin recognition an important, rapidly moving discipline within behavioural ecology.     

Distinguishing the roles of dispersal in diversity maintenance and in diversity limitation

Considerable recent research effort has gone into studying how dispersal might affect the diversity of local communities. While this general topic has received attention from theoretical and empirical ecologists alike, the research focus has differed between the two groups; theoretical ecologists have explored the role of dispersal in the maintenance of diversity within local communities, whereas empirical ecologists have sought to quantify the role of dispersal in limiting local diversity. We argue that there is no necessary relationship between these two components of diversity and we therefore need to develop empirical approaches to quantify the dispersal-maintained component of diversity, as well as the dispersal-limited component. We develop one such approach in this paper, based on a quantitative partitioning of the natural regeneration within intact communities onto different sources of recruits (local communityvs. dispersal across different spatial or temporal scales).     

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.     

生态位分化与森林群落物种多样性维持研究展望

一直以来,生态学家和进化生物学家对森林群落物种多样格局及其形成机制持有不同的观点。虽然Robert Ricklefs将进化和生态过程整合的观点已经被群落生态学家广泛接受,但是区域物种进化历史以及局域群落微进化过程是否能够影响群落生态学过程以及这些过程如何影响群落结构和动态还有待商榷。经典的生态位理论同时强调了种间和种内生态位分化对群落多样性维持的影响。但是生态学家普遍认为种间差异足以代表群落内个体间的相互作用关系,并且由于进化过程导致的种内分化往往涉及较长的时间尺度,因此,虽然种内差异是自然选择的重要材料,物种对环境的适应性进化过程所导致的种内分化对群落构建的影响往往被生态学家所忽视。为此,通过回顾种间和个体生态位分化的研究历史,对两类研究分别进行简要阐述,强调在今后的群落生态学研究中需要考虑个体分化对局域群落构建的影响。     

A Toolkit for Ecosystem Ecologists in the Time of Big Science

Ecosystem ecologists are being challenged to address the increasingly complex problems that comprise Big Science. These problems include multiple levels of biological organization that cross multiple interacting temporal and spatial scales, from individual plants, animals, and microbes to landscapes, continents, and the globe. As technology improves, the availability of data, derived data products, and information to address these complex problems are increasing at finer and coarser scales of resolution, and legacy, dark data are brought to light. Data analytics are improving as big data increase in importance in other fields that are improving access to these data. New data sources (crowdsourcing, social media) and ease of communication and collaboration among ecosystem ecologists and other disciplines are increasingly possible via the internet. It is increasingly important that ecosystem ecologists be able to communicate their findings, and to translate their concepts and findings into concrete bits of information that a general public can understand. Traditional approaches that portray ecosystem sciences as a dichotomy between empirical research and theoretical research will keep the field from fully contributing to the complexity of global change questions, and will keep ecosystem ecologists from taking full advantage of the data and technology available. Building on previous research, we describe a more forward-looking, integrated empirical–theoretical modeling approach that is iterative with learning to take advantage of the elements of Big Science. We suggest that training ecosystem ecologists in this integrated approach will be critical to addressing complex Earth system science questions, now and in the future.     

Progress in invasion biology: predicting invaders

Predicting which species are probable invaders has been a long-standing goal of ecologists, but only recently have quantitative methods been used to achieve such a goal. Although restricted to few taxa, these studies reveal clear relationships between the characteristics of releases and the species involved, and the successful establishment and spread of invaders. For example, the probability of bird establishment increases with the number of individuals released and the number of release events. Also, the probability of plant invasiveness increases if the species has a history of invasion and reproduces vegetatively. These promising quantitative approaches should be more widely applied to allow us to predict patterns of invading species more successfully.     

Bayesian data analysis in population ecology: motivations,methods, and benefits

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.