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Predator–prey relationships are a central component of community dynamics. Classic approaches have tried to understand and predict these relationships in terms of consumptive interactions between predator and prey species, but characterizing the interaction this way is insufficient to predict the complexity and context dependency inherent in predator–prey relationships. Recent approaches have begun to explore predator–prey relationships in terms of an evolutionary-ecological game in which predator and prey adapt to each other through reciprocal interactions involving context-dependent expression of functional traits that influence their biomechanics. Functional traits are defined as any morphological, behavioral, or physiological trait of an organism associated with a biotic interaction. Such traits include predator and prey body size, predator and prey personality, predator hunting mode, prey mobility, prey anti-predator behavior, and prey physiological stress. Here, I discuss recent advances in this functional trait approach. Evidence shows that the nature and strength of many interactions are dependent upon the relative magnitude of predator and prey functional traits. Moreover, trait responses can be triggered by non-consumptive predator–prey interactions elicited by responses of prey to risk of predation. These interactions in turn can have dynamic feedbacks that can change the context of the predator–prey interaction, causing predator and prey to adapt their traits—through phenotypically plastic or rapid evolutionary responses—and the nature of their interaction. Research shows that examining predator–prey interactions through the lens of an adaptive evolutionary-ecological game offers a foundation to explain variety in the nature and strength of predator–prey interactions observed in different ecological contexts.

Metacommunity theory has provided many insights into the general problem of local versus regional control of species diversity and relative abundance. The metacommunity framework has been extended from competitive interactions to whole food webs that can be described as spatial networks of interaction networks. Trophic metacommunity theory greatly contributed to resolving the community complexity-stability debate by predicting its dependence on the regional spatial context. The meta-ecosystem framework has since been suggested as a useful simplification of complex ecosystems to apply this spatial context to spatial flows of both individuals and matter. Reviewing the recent literature on metacommunity and meta-ecosystem theories suggests the importance of unifying theories of interaction strength into a meta-ecosystem framework that captures how the strength of spatial, species, and ecosystem fluxes are distributed across location and trophic levels. Such integration predicts important feedback between local and regional processes that drive the assembly of species, the stability of community, and the emergence of ecosystem functions, from limited spatial fluxes of individuals and (in)organic matter. These predictions are often mediated by the maintenance of environmental or endogenous fluctuations from local to regional scales that create important challenges and opportunities for the validation of metacommunity and meta-ecosystem theories and their application to conservation.

Despite the progress that has been made in many other aspects of data visualisation, phylogenies are still represented in much the same way as they first were by Darwin. In this brief essay, I give a short review of what I consider to be some recent major advances, and outline a new kind of phylogenetic visualisation. This new graphic, the fibre plot, uses the metaphor of sections through a tree to describe change in a phylogeny. I suggest it is a useful tool in gaining an rapid overview of the timing and scale of diversification in large phylogenies.

Around the world, governments are establishing Marine Protected Area (MPA) networks to meet their commitments to the United Nations Convention on Biological Diversity. MPAs are often used in an effort to conserve biodiversity and manage fisheries stocks. However, their efficacy and effect on fisheries yields remain unclear. We conducted a case-study on the economic impact of different MPA network design strategies on the Atlantic cod ( Gadus morhua) fisheries in Canada. The open-source R package that we developed to analyze this case study can be customized to conduct similar analyses for other systems. We used a spatially-explicit individual-based model of population growth and dispersal coupled with a fisheries management and harvesting component. We found that MPA networks that both protect the target species’ habitat and were spatially optimized to improve population connectivity had the highest net present value (i.e., were most profitable for the fishing industry). These higher profits were achieved primarily by reducing the distance travelled for fishing and reducing the probability of a moratorium event. These findings add to a growing body of knowledge demonstrating the importance of incorporating population connectivity in the MPA planning process, as well as the ability of this R package to explore ecological and economic consequences of alternative MPA network designs.

Plant-herbivore interactions shape community dynamics across marine, freshwater, and terrestrial habitats. From amphipods to elephants and from algae to trees, plant-herbivore relationships are the crucial link generating animal biomass (and human societies) from mere sunlight. These interactions are, thus, pivotal to understanding the ecology and evolution of virtually any ecosystem. Here, we briefly highlight recent advances in four areas of plant-herbivore interactions: (1) plant defense theory, (2) herbivore diversity and ecosystem function, (3) predation risk aversion and herbivory, and (4) how a changing climate impacts plant-herbivore interactions. Recent advances in plant defense theory, for example, highlight how plant life history and defense traits affect and are affected by multiple drivers, including enemy pressure, resource availability, and the local plant neighborhood, resulting in trait-mediated feedback loops linking trophic interactions with ecosystem nutrient dynamics. Similarly, although the positive effect of consumer diversity on ecosystem function has long been recognized, recent advances using DNA barcoding to elucidate diet, and Global Positioning System/remote sensing to determine habitat selection and impact, have shown that herbivore communities are probably even more functionally diverse than currently realized. Moreover, although most diversity-function studies continue to emphasize plant diversity, herbivore diversity may have even stronger impacts on ecosystem multifunctionality. Recent studies also highlight the role of risk in plant-herbivore interactions, and risk-driven trophic cascades have emerged as landscape-scale patterns in a variety of ecosystems. Perhaps not surprisingly, many plant-herbivore interactions are currently being altered by climate change, which affects plant growth rates and resource allocation, expression of chemical defenses, plant phenology, and herbivore metabolism and behavior. Finally, we conclude by noting that although the field is advancing rapidly, the world is changing even more rapidly, challenging our ability to manage these pivotal links in the food chain.

Size varies. Small things are typically more frequent than large things. The logarithm of frequency often declines linearly with the logarithm of size. That power law relation forms one of the common patterns of nature. Why does the complexity of nature reduce to such a simple pattern? Why do things as different as tree size and enzyme rate follow similarly simple patterns? Here I analyze such patterns by their invariant properties. For example, a common pattern should not change when adding a constant value to all observations. That shift is essentially the renumbering of the points on a ruler without changing the metric information provided by the ruler. A ruler is shift invariant only when its scale is properly calibrated to the pattern being measured. Stretch invariance corresponds to the conservation of the total amount of something, such as the total biomass and consequently the average size. Rotational invariance corresponds to pattern that does not depend on the order in which underlying processes occur, for example, a scale that additively combines the component processes leading to observed values. I use tree size as an example to illustrate how the key invariances shape pattern. A simple interpretation of common pattern follows. That simple interpretation connects the normal distribution to a wide variety of other common patterns through the transformations of scale set by the fundamental invariances.

Sustainability is a key challenge for humanity in the 21st century. Ecosystem services—the benefits that people derive from nature and natural capital—is a concept often used to help explain human reliance on nature and frame the decisions we make in terms of the ongoing value of nature to human wellbeing. Yet ecosystem service science has not always lived up to the promise of its potential. Despite advances in the scientific literature, ecosystem service science has not yet answered some of the most critical questions posed by decision-makers in the realm of sustainability. Here, we explore the history of ecosystem service science, discuss advances in conceptualization and measurement, and point toward further work needed to improve the use of ecosystem service in decisions about sustainable development.

Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

Kin selection and multilevel selection are two major frameworks in evolutionary biology that aim at explaining the evolution of social behaviors. However, the relationship between these two theories has been plagued by controversy for almost half a century and debates about their relevance and usefulness in explaining social evolution seem to rekindle at regular intervals. Here, we first provide a concise introduction into the kin selection and multilevel selection theories and shed light onto the roots of the controversy surrounding them. We then review two major aspects of the current debate: the presumed formal equivalency of the two theories and the question whether group selection can lead to group adaptation. We conclude by arguing that the two theories can offer complementary approaches to the study of social evolution: kin selection approaches usually focus on the identification of optimal phenotypes and thus on the endresult of a selection process, whereas multilevel selection approaches focus on the ongoing selection process itself. The two theories thus provide different perspectives that might be fruitfully combined to promote our understanding of the evolution in group-structured populations.

Forest dynamics encompass changes in stand structure, species composition, and species interactions with disturbance and environment over a range of spatial and temporal scales. For convenience, spatial scale is defined as individual tree, neighborhood, stand, and landscape. Whether a given canopy-leveling disturbance will initiate a sequence of development in structure with little change in composition or initiate an episode of succession depends on a match or mismatch, respectively, with traits of the dominant tree species that allow the species to survive disturbance. When these match, certain species-disturbance type combinations lock in a pattern of stand and landscape dynamics that can persist for several generations of trees; thus, dominant tree species regulate, as well as respond to, disturbance. A complex interaction among tree species, neighborhood effects, disturbance type and severity, landform, and soils determines how stands of differing composition form and the mosaic of stands that compose the landscape. Neighborhood effects (e.g., serotinous seed rain, sprouting, shading, leaf-litter chemistry, and leaf-litter physical properties) operate at small spatial extents of the individual tree and its neighbors but play a central role in forest dynamics by contributing to patch formation at stand scales and dynamics of the entire landscape. Dominance by tree species with neutral to negative neighborhood effects leads to unstable landscape dynamics in disturbance-prone regions, wherein most stands are undergoing succession; stability can only occur under very low-severity disturbance regimes. Dominance by species with positive effects leads to stable landscape dynamics wherein only a small proportion of stands undergo succession at any one time. Positive neighborhood effects are common in temperate and boreal zones, whereas negative effects are more common in tropical climates. Landscapes with positive dynamics have alternate categories of dynamics stabilized by high-severity and low-severity disturbance regimes. Contrary to prevailing ecological theory, systems with positive neighborhood effects can have similar levels of compositional stability across tree, stand, and landscape scales. Neighborhood effect theory can help explain responses of landscapes to large-scale land clearing and novel effects brought on by factors such as invasive species and deer overabundance.

Ecological intensification has been proposed as a paradigm for ensuring global food security while preserving biodiversity and ecosystem integrity. Ecological intensification was originally coined to promote precise site-specific farming practices aimed at reducing yield gaps, while avoiding negative environmental impacts ( techno-simplicity). Recently, it has been extended to stress the importance of landscape complexity to preserve biodiversity and ecosystem services ( eco-complexity). While these perspectives on ecological intensification may seem distinct, they are not incompatible and should be interwoven to create more comprehensive and practical solutions. Here, we argue that designing cropping systems to be more diverse, across space and time would be an effective route to accomplish environmentally-friendly intensification of crop production. Such a novel approach will require better integration of knowledge at the landscape level for increasing agro-biodiversity (focused on interventions outside fields) with strategies diversifying cropping systems to manage weeds and pests (focused on interventions inside fields).

In 2004, the Atlantic Ecology Division of the US Environmental Protection Agency’s Office of Research and Development began an annual winter waterfowl survey of Rhode Island’s Narragansett Bay. Herein, we explore the survey data gathered from 2004 to 2011 in order to establish a benchmark understanding of our waterfowl communities and to establish a statistical framework for future environmental monitoring. The abundance and diversity of wintering waterfowl were relatively stable during the initial years of this survey, except in 2010 when there was a large spike in abundance and a reciprocal fall in diversity. There was no significant change in ranked abundance of most waterfowl species, with only Bufflehead ( Bucephala albeola) and Hooded Merganser ( Lophodytes cucllatus) showing a slight yet significant upward trend during the course of our survey period. Nonmetric multidimensional scaling (NMDS) was used to examine the community structure of wintering waterfowl. The results of the NMDS indicate that there is a spatial structure to the waterfowl communities of Narragansett Bay and this structure has remained relatively stable since the survey began. Our NMDS analysis helps to solidify what is known anecdotally about the bay’s waterfowl ecology, and provides a formalized benchmark for long-term monitoring of Narragansett Bay’s waterfowl communities. Birds, including waterfowl, are preferred bioindicators and we propose using our multivariate approach to monitor the future health of the bay. While this research focuses on a specific area of New England, these methods can be easily applied to novel areas of concern and provide a straightforward nonparametric approach to community-level monitoring. The methods provide a statistic test to examine potential drivers of community turnover and well-suited visualization tools.

An increased understanding of the current and potential future impacts of climate change has significantly influenced conservation in practice in recent years. Climate change has necessitated a shift toward longer planning time horizons, moving baselines, and evolving conservation goals and targets. This shift has resulted in new perspectives on, and changes in, the basic approaches practitioners use to conserve biodiversity. Restoration, spatial planning and reserve selection, connectivity modelling, extinction risk assessment, and species translocations have all been reimagined in the face of climate change. Restoration is being conducted with a new acceptance of uncertainty and an understanding that goals will need to shift through time. New conservation targets, such as geophysical settings and climatic refugia, are being incorporated into conservation plans. Risk assessments have begun to consider the potentially synergistic impacts of climate change and other threats. Assisted colonization has gained acceptance in recent years as a viable and necessary conservation tool. This evolution has paralleled a larger trend in conservation—a shift toward conservation actions that benefit both people and nature. As we look forward, it is clear that more change is on the horizon. To protect biodiversity and essential ecosystem services, conservation will need to anticipate the human response to climate change and to focus not only on resistance and resilience but on transitions to new states and new ecosystems.

With scientific and societal interest in biodiversity impacts of climate change growing enormously over the last decade, we analysed directions and biases in the recent most highly cited data papers in this field of research (from 2012 to 2014). The majority of this work relied on leveraging large databases of already collected historical information (but not paleo- or genetic data), and coupled these to new methodologies for making forward projections of shifts in species’ geographical ranges, with a focus on temperate and montane plants. A consistent finding was that the pace of climate-driven habitat change, along with increased frequency of extreme events, is outpacing the capacity of species or ecological communities to respond and adapt.

In 2004, the Atlantic Ecology Division of the US Environmental Protection Agency’s Office of Research and Development began an annual winter waterfowl survey of Rhode Island’s Narragansett Bay. Herein, we explore the survey data gathered from 2004 to 2011 in order to establish a benchmark understanding of our waterfowl communities and to establish a statistical framework for future environmental monitoring. The abundance and diversity of wintering waterfowl were relatively stable during the initial years of this survey, except in 2010 when there was a large spike in abundance and a reciprocal fall in diversity. There was no significant change in ranked abundance of most waterfowl species, with only Bufflehead ( Bucephala albeola) and Hooded Merganser ( Lophodytes cucllatus) showing a slight yet significant upward trend during the course of our survey period. Nonmetric multidimensional scaling (NMDS) was used to examine the community structure of wintering waterfowl. The results of the NMDS indicate that there is a spatial structure to the waterfowl communities of Narragansett Bay and this structure has remained relatively stable since the survey began. Our NMDS analysis helps to solidify what is known anecdotally about the bay’s waterfowl ecology, and provides a formalized benchmark for long-term monitoring of Narragansett Bay’s waterfowl communities. Birds, including waterfowl, are preferred bioindicators and we propose using our multivariate approach to monitor the future health of the bay. While this research focuses on a specific area of New England, these methods can be easily applied to novel areas of concern and provide a straightforward nonparametric approach to community-level monitoring. The methods provide a statistic test to examine potential drivers of community turnover and well-suited visualization tools.

In 2004, the Atlantic Ecology Division of the US Environmental Protection Agency’s Office of Research and Development began an annual winter waterfowl survey of Rhode Island’s Narragansett Bay. Herein, we explore the survey data gathered from 2004 to 2011 in order to establish a benchmark understanding of the Narragansett Bay’s waterfowl communities and to establish a statistical framework for future environmental monitoring. Abundance and diversity of Narragansett Bay’s wintering waterfowl were relatively stable during the initial years of this survey, except in 2010 when there was a large spike in abundance and a reciprocal fall in diversity. There was no significant change in ranked abundance of most waterfowl species, with only Bufflehead ( Bucephala albeola) and Hooded Merganser ( Lophodytes cucllatus) showing a slight yet significant upward trend during the course of our survey period. Nonmetric multidimensional scaling was used to examine the community structure of wintering waterfowl in Narragansett Bay. The results of the Nonmetric multidimensional scaling indicate that there is a spatial structure to the waterfowl communities of Narragansett Bay and this structure has remained relatively stable since the survey began. Our Nonmetric multidimensional scaling analysis helps to solidify what is known anecdotally about Narragansett Bay’s waterfowl ecology, and provides a formalized benchmark for long-term monitoring of Narragansett Bay’s waterfowl communities. Birds, including waterfowl, are preferred bioindicators and we propose using our multivariate approach to monitor the future health of Narragansett Bay.

Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an “adaptive ensemble” method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts.

Multifractals have been applied to characterize complex communities in a spatial context. They were developed for nonlinear systems and are particularly suited to capture multiplicative processes observed in ecological systems. Multifractals characterize variability in a scale-independent way within an experimental range. I have developed an open-source software package to estimate multifractals using a box-counting algorithm (available from https://github.com/lsaravia/mfsba and permanently available at doi: 10.5281/zenodo.8481). The software is specially designed for two dimensional (2D) images such as the ones obtained from remote sensing, but other 2D data types can also be analyzed. Additionally I developed a new metric to analyze multispecies spatial patterns with multifractals: spatial rank surface, which is included in the software.

Multifractals have been applied to characterize complex communities in a spatial context. They were developed for nonlinear systems and are particularly suited to capture multiplicative processes observed in ecological systems. Multifractals characterize variability in a scale-independent way within an experimental range. I have developed an open-source software package to estimate multifractals using a box-counting algorithm (available from https://github.com/lsaravia/mfsba and permanently available at doi: 10.5281/zenodo.7659). The software is specially designed for two dimensional (2D) images such as the ones obtained from remote sensing, but other 2D data types can also be analyzed. Additionally I developed a new metric to analyze multispecies spatial patterns with multifractals: spatial rank surface, which is included in the software.

Measuring the modularity of networks, and how it deviates from random expectations, important to understand their structure and emerging properties. Several measures exist to assess modularity, which when applied to the same network, can return both different modularity values (i.e. different estimates of how modular the network is) and different module compositions (i.e. different groups of species forming said modules). More importantly, as each optimization method uses a different optimization criterion, there is a need to have an a posteriori measure serving as an equivalent of a goodness-of-fit. In this article, I propose such a measure of modularity, which is simply defined as the ratio of interactions established between members of the same modules vs. members of different modules. I apply this measure to a large dataset of 290 ecological networks representing host–parasite (bipartite) and predator–prey (unipartite) interactions, to show how the results are easy to interpret and present especially to a broad audience not familiar with modularity analyses, but still can reveal new features about modularity and the ways to measure it.

Multifractals have been applied to characterize complex communities in a spatial context. They were developed for nonlinear systems and are particularly suited to capture multiplicative processes observed in ecological systems. Multifractals characterize variability in a scale-independent way within an experimental range. I have developed an open-source software package to estimate multifractals using a box-counting algorithm (available from https://github.com/lsaravia/mfsba and permanently available at doi: 10.5281/zenodo.8481). The software is specially designed for two dimensional (2D) images such as the ones obtained from remote sensing, but other 2D data types can also be analyzed. Additionally I developed a new metric to analyze multispecies spatial patterns with multifractals: spatial rank surface, which is included in the software.

Measuring the modularity of networks, and how it deviates from random expectations, important to understand their structure and emerging properties. Several measures exist to assess modularity, which when applied to the same network, can return both different modularity values (i.e. different estimates of how modular the network is) and different module compositions (i.e. different groups of species forming said modules). More importantly, as each optimization method uses a different optimization criterion, there is a need to have an a posteriori measure serving as an equivalent of a goodness-of-fit. In this article, I propose such a measure of modularity, which is simply defined as the ratio of interactions established between members of the same modules vs. members of different modules. I apply this measure to a large dataset of 290 ecological networks representing host–parasite (bipartite) and predator–prey (unipartite) interactions, to show how the results are easy to interpret and present especially to a broad audience not familiar with modularity analyses, but still can reveal new features about modularity and the ways to measure it.

Measuring modularity is important to understand the structure of networks, and has an important number of real-world implications. However, several measures exists to assess the modularity, and give both different modularity values and different modules composition. In this article, I propose an a posteriori measure of modularity, which represents the ratio of interactions between members of the same modules vs. members of different modules. I apply this measure to a large dataset of 290 ecological networks, to show that it gives new insights about their modularity.

Measuring modularity is important to understand the structure of networks, and has an important number of real-world implications. However, several measures exists to assess the modularity, and give both different modularity values and different modules composition. In this article, I propose an a posteriori measure of modularity, which represents the ratio of interactions between members of the same modules vs. members of different modules. I apply this measure to a large dataset of 290 ecological networks, to show that it gives new insights about their modularity.