Defining and measuring ecological specialization

1.  Ecological specialization is one of the main concepts in ecology and conservation. However, this concept has become highly context-dependent and is now obscured by the great variability of existing definitions and methods used to characterize ecological specialization.
2.  In this study, we clarify this concept by reviewing the strengths and limitations of different approaches commonly used to define and measure ecological specialization. We first show that ecological specialization can either be considered as reflecting species' requirements or species' impacts. We then explain how specialization depends on species-specific characteristics and on local and contingent environmental constraints. We further show why and how ecological specialization should be scaled across spatial and temporal scales, and from individuals to communities.
3.  We then illustrate how this review can be used as a practical toolbox to classify widely used metrics of ecological specialization in applied ecology, depending on the question being addressed, the method used, and the data available.
4.   Synthesis and applications . Clarifying ecological specialization is useful to make explicit connections between several fields of ecology using the niche concept. Defining this concept and its practical metrics is also a crucial step to better formulate predictions of scientific interest in ecology and conservation. Finally, understanding the different facets of ecological specialization should facilitate to investigate the causes and consequences of biotic homogenization and to derive relevant indicators of biodiversity responses to land-use changes.     

How can we harness quantitative genetic variation in crop root systems for agricultural improvement?

Root systems are a black box obscuring a comprehensive understanding of plant function,from the ecosystem scale down to the individual. In particular,a lack of knowledge about the genetic mechanisms and environmental effects that condition root system growth hinders our ability to develop the next generation of crop plants for improved agricultural productivity and sustainability. We discuss how the methods and metrics we use to quantify root systems can affect our ability to understand them,how we can bridge knowledge gaps and accelerate the derivation of structurefunction relationships for roots,and why a detailed mechanistic understanding of root growth and function will be important for future agricultural gains.     

Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.     

Decay of interspecific avian flock networks along a disturbance gradient in Amazonia

Our understanding of how anthropogenic habitat change shapes species interactions is in its infancy. This is in large part because analytical approaches such as network theory have only recently been applied to characterize complex community dynamics. Network models are a powerful tool for quantifying how ecological interactions are affected by habitat modification because they provide metrics that quantify community structure and function. Here, we examine how large-scale habitat alteration has affected ecological interactions among mixed-species flocking birds in Amazonian rainforest. These flocks provide a model system for investigating how habitat heterogeneity influences non-trophic interactions and the subsequent social structure of forest-dependent mixed-species bird flocks. We analyse 21 flock interaction networks throughout a mosaic of primary forest, fragments of varying sizes and secondary forest (SF) at the Biological Dynamics of Forest Fragments Project in central Amazonian Brazil. Habitat type had a strong effect on network structure at the levels of both species and flock. Frequency of associations among species, as summarized by weighted degree, declined with increasing levels of forest fragmentation and SF. At the flock level, clustering coefficients and overall attendance positively correlated with mean vegetation height, indicating a strong effect of habitat structure on flock cohesion and stability. Prior research has shown that trophic interactions are often resilient to large-scale changes in habitat structure because species are ecologically redundant. By contrast, our results suggest that behavioural interactions and the structure of non-trophic networks are highly sensitive to environmental change. Thus, a more nuanced, system-by-system approach may be needed when thinking about the resiliency of ecological networks.     

Ecological memory at millennial time-scales: the importance of data constraints,species longevity and niche features

Ecological memory describes how antecedent conditions drive the dynamics of an ecological system. Palaeoecological records are paramount to understand ecological memory at millennial time-scales, but the concept is widely neglected in the literature, and a formal approach is lacking. Here, we fill such a gap by introducing a quantitative framework for ecological memory in palaeoecology, and assessing how data constraints and taxa traits shape ecological memory patterns. We simulate the population dynamics and pollen abundance of 16 virtual taxa with different life and niche traits as a response to an environmental driver. The data is processed to mimic a realistic sediment deposition and sampled at increasing depth intervals. We quantify ecological memory with Random Forests, and assess how data properties and taxa traits shape ecological memory. We find that life-span and niche features modulate the relative importance of the antecedent values of the driver and the pollen abundance over periods of 240 yr and longer. Additionally, we find that accumulation rate and decreasing pollen-sampling resolution inflate the importance of antecedent pollen abundance. Our results suggest that: 1) ecological memory patterns are sensitive to varying accumulation rates. A better understanding on the numerical basis of this effect may enable the assimilation of ecological memory concepts and methods in palaeoecology; 2) incorporating niche theory and models is essential to better understand the nature of ecological memory patterns at millennial time-scales. 3) Long-lived generalist taxa are highly decoupled from the environmental signal. This finding has implications on how we interpret the abundance-environment relationship of real taxa with similar traits, and how we use such knowledge to forecast their distribution or reconstruct past climate.     

Winning and losing with microbes: how microbially mediated fitness differences influence plant diversity

Interactions between plants and soil microbes can strongly influence plant diversity and community dynamics. Soil microbes may promote plant diversity by driving negative frequency‐dependent plant population dynamics, or may favor species exclusion by providing one species an average fitness advantage over others. However, past empirical research has focused overwhelmingly on the consequences of frequency‐dependent feedbacks for plant species coexistence and has generally neglected the consequences of microbially mediated average fitness differences. Here we use theory to develop metrics that quantify microbially mediated plant fitness differences, and show that accounting for these effects can profoundly change our understanding of how microbes influence plant diversity. We show that soil microbes can generate fitness differences that favour plant species exclusion when they disproportionately harm (or favour) one plant species over another, but these fitness differences may also favor coexistence if they trade off with competition for other resources or generate intransitive dominance hierarchies among plants. We also show how the metrics we present can quantify microbially mediated fitness differences in empirical studies, and explore how microbial control over coexistence varies along productivity gradients. In all, our analysis provides a more complete theoretical foundation for understanding how plant–microbe interactions influence plant diversity.     

Measuring complexity to infer changes in the dynamics of ecological systems under stress

Despite advances in our mechanistic understanding of ecological processes, the inherent complexity of real-world ecosystems still limits our ability in predicting ecological dynamics especially in the face of on-going environmental stress. Developing a model is frequently challenged by structure uncertainty, unknown parameters, and limited data for exploring out-of-sample predictions. One way to address this challenge is to look for patterns in the data themselves in order to infer the underlying processes of an ecological system rather than to build system-specific models. For example, it has been recently suggested that statistical changes in ecological dynamics can be used to infer changes in the stability of ecosystems as they approach tipping points. For computer scientists such inference is similar to the notion of a Turing machine: a computational device that could execute a program (the process) to produce the observed data (the pattern). Here, we make use of such basic computational ideas introduced by Alan Turing to recognize changing patterns in ecological dynamics in ecosystems under stress. To do this, we use the concept of Kolmogorov algorithmic complexity that is a measure of randomness. In particular, we estimate an approximation to Kolmogorov complexity based on the Block Decomposition Method (BDM). We apply BDM to identify changes in complexity in simulated time-series and spatial datasets from ecosystems that experience different types of ecological transitions. We find that in all cases, K_BDM complexity decreased before all ecological transitions both in time-series and spatial datasets. These trends indicate that loss of stability in the ecological models we explored is characterized by loss of complexity and the emergence of a regular and computable underlying structure. Our results suggest that Kolmogorov complexity may serve as tool for revealing changes in the dynamics of ecosystems close to ecological transitions.     

Pathogen pressure puts immune defense into perspective

The extent to which organisms can protect themselves from disease depends on both the immune defenses they maintain and the pathogens they face. At the same time, immune systems are shaped by the antigens they encounter, both over ecological and evolutionary time. Ecological immunologists often recognize these interactions, yet ecological immunology currently lacks major efforts to characterize the environmental, host-independent, antigenic pressures to which all animals are exposed. Failure to quantify relevant diseases and pathogens in studies of ecological immunology leads to contradictory hypotheses. In contrast, including measures of environmental and host-derived commensals, pathogens, and other immune-relevant organisms will strengthen the field of ecological immunology. In this article, we examine how pathogens and other organisms shape immune defenses and highlight why such information is essential for a better understanding of the causes of variation in immune defenses. We introduce the concept of "operative protection" for understanding the role of immunologically relevant organisms in shaping immune defense profiles, and demonstrate how the evolutionary implications of immune function are best understood in the context of the pressures that diseases and pathogens bring to bear on their hosts. We illustrate common mistakes in characterizing these immune-selective pressures, and provide suggestions for the use of molecular and other methods for measuring immune-relevant organisms.     

On the importance of intraspecific variability for the quantification of functional diversity

Functional diversity (FD) is a key facet of biodiversity used to address central questions in ecology. Despite recent methodological advances, FD remains a complex concept and no consensus has been reached either on how to quantify it, or on how it influences ecological processes. Here we define FD as the distribution of trait values within a community. When and how to account for intraspecific trait variability (ITV) when measuring FD remains one of the main current debates. It remains however unclear to what extent accounting for population‐level ITV would modify FD quantification and associated conclusions. In this paper, we address two critical questions: (1) How sensitive are different components of FD to the inclusion of population‐level ITV? (2) Does the omission of ITV obscure the understanding of ecological patterns? Using a mixture of empirical data and simulation experiments, we conducted a sensitivity analysis of four commonly used FD indices (community weighted mean traits, functional richness, Rao's quadratic entropy, Petchey and Gaston's FD index) and their relationships with environmental gradients and species richness, by varying both the extent (plasticity or not) and the structure (contingency to environmental gradient due to local adaptation) of population‐level ITV. Our results suggest that ITV may strongly alter the quantification of FD and the detection of ecological patterns. Our analysis highlights that 1) species trait values distributions within communities are crucial to the sensitivity to ITV, 2) ITV structure plays a major role in this sensitivity and 3) different indices are not evenly sensitive to ITV, the single‐trait FD from Petchey and Gaston being the most sensitive among the four metrics tested. We conclude that the effects of intraspecific variability in trait values should be more systematically tested before drawing central conclusions on FD, and suggest the use of simulation studies for such sensitivity analyses.     

A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement

Nestedness has been widely reported for both metacommunities and networks of interacting species. Even though the concept of this ecological pattern has been well-defined, there are several metrics by which it can be quantified. We noted that current metrics do not correctly quantify two major properties of nestedness: (1) whether marginal totals (i.e. fills) differ among columns and/or among rows, and (2) whether the presences (1's) in less-filled columns and rows coincide, respectively, with those found in the more-filled columns and rows. We propose a new metric directly based on these properties and compare its behavior with that of the most used metrics, using a set of model matrices ranging from highly-nested to alternative structures in which no nestedness should be detected. We also used an empirical dataset to explore possible biases generated by the metrics as well as to evaluate correlations between metrics. We found that nestedness has been quantified by metrics that inappropriately detect this pattern, even for matrices in which there is no nestedness. In addition, the most used metrics are prone to type I statistical errors while our new metric has better statistical properties and consistently rejects a nested pattern for different types of random matrices. The analysis of the empirical data showed that two nestedness metrics, matrix temperature and the discrepancy measure, tend to overestimate the degrees of nestedness in metacommunities. We emphasize and discuss some implications of these biases for the theoretical understanding of the processes shaping species interaction networks and metacommunity structure.     

Ecological implications of behavioural syndromes

Interspecific trait variation has long served as a conceptual foundation for our understanding of ecological patterns and dynamics. In particular, ecologists recognise the important role that animal behaviour plays in shaping ecological processes. An emerging area of interest in animal behaviour, the study of behavioural syndromes (animal personalities) considers how limited behavioural plasticity, as well as behavioural correlations affects an individual's fitness in diverse ecological contexts. In this article we explore how insights from the concept and study of behavioural syndromes provide fresh understanding of major issues in population ecology. We identify several general mechanisms for how population ecology phenomena can be influenced by a species or population's average behavioural type, by within-species variation in behavioural type, or by behavioural correlations across time or across ecological contexts. We note, in particular, the importance of behavioural type-dependent dispersal in spatial ecology. We then review recent literature and provide new syntheses for how these general mechanisms produce novel insights on five major issues in population ecology: (1) limits to species' distribution and abundance; (2) species interactions; (3) population dynamics; (4) relative responses to human-induced rapid environmental change; and (5) ecological invasions.     

On the dimensionality of ecological stability

Ecological stability is touted as a complex and multifaceted concept, including components such as variability, resistance, resilience, persistence and robustness. Even though a complete appreciation of the effects of perturbations on ecosystems requires the simultaneous measurement of these multiple components of stability, most ecological research has focused on one or a few of those components analysed in isolation. Here, we present a new view of ecological stability that recognises explicitly the non‐independence of components of stability. This provides an approach for simplifying the concept of stability. We illustrate the concept and approach using results from a field experiment, and show that the effective dimensionality of ecological stability is considerably lower than if the various components of stability were unrelated. However, strong perturbations can modify, and even decouple, relationships among individual components of stability. Thus, perturbations not only increase the dimensionality of stability but they can also alter the relationships among components of stability in different ways. Studies that focus on single forms of stability in isolation therefore risk underestimating significantly the potential of perturbations to destabilise ecosystems. In contrast, application of the multidimensional stability framework that we propose gives a far richer understanding of how communities respond to perturbations.     

A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape

Graph structures and habitat availability metrics are two recent and complementary approaches for analysing landscape connectivity. They have gained rapid popularity and provided significant conceptual improvements for decision making in conservation planning. We present a further methodological development of the habitat availability concept and metrics by partitioning them into three separate fractions that quantify the different ways in which individual landscape elements can contribute to overall habitat connectivity and availability in the landscape, including stepping stone effects. These fractions are derived from the same concept, are measured in the same units and can be directly compared and combined within a unifying framework. This avoids the problematic and, so far, usual combination of metrics coming from different backgrounds and the arbitrary weighting of connectivity considerations in a broader context of conservation alternatives. We analyse how the relative importance of each fraction varies with species traits. In addition, we show how the critical patches differ for each of the fractions by analysing various forest habitats in the province of Lleida (NE Spain). We discuss the conceptual and conservation implications of this approach, which can be adapted to different degrees of ecological and spatial detail within the graph while still maintaining a coherent framework for the identification of critical elements in the landscape network.     

Quantifying uncertainties in biologically-based water quality assessment: A pan-European analysis of lake phytoplankton community metrics

Lake phytoplankton are adopted world-wide as a sensitive indicator of water quality. European environmental legislation, the EU Water Framework Directive (WFD), formalises this, requiring the use of phytoplankton to assess the ecological status of lakes and coastal waters. Here we provide a rigorous assessment of a number of proposed phytoplankton metrics for assessing the ecological quality of European lakes, specifically in response to nutrient enrichment, or eutrophication, the most widespread pressure affecting lakes. To be useful indicators, metrics must have a small measurement error relative to the eutrophication signal we want them to represent among lakes of different nutrient status. An understanding of variability in metric scores among different locations around a lake, or due to sampling and analytical variability can also identify how best this measurement error is minimised.To quantify metric variability, we analyse data from a multi-scale field campaign of 32 European lakes, resolving the extent to which seven phytoplankton metrics (including chlorophyll a, the most widely used metric of lake quality) vary among lakes, among sampling locations within a lake and through sample replication and processing. We also relate these metrics to environmental variables, including total phosphorus concentration as an indicator of eutrophication.For all seven metrics, 65–96% of the variance in metric scores was among lakes, much higher than variability occurring due to sampling/sample processing. Using multi-model inference, there was strong support for relationships between among-lake variation in three metrics and differences in total phosphorus concentrations. Three of the metrics were also related to mean lake depth. Variability among locations within a lake was minimal (<4%), with sub-samples and analysts accounting for much of the within-lake metric variance. This indicates that a single sampling location is representative and suggests that sub-sample replication and standardisation of analyst procedures should result in increased precision of ecological assessments based upon these metrics.For three phytoplankton metrics being used in the WFD: chlorophyll a concentration, the Phytoplankton Trophic Index (PTI) and cyanobacterial biovolume, >85% of the variance in metric scores was among-lakes and total phosphorus concentration was well supported as a predictor of this variation. Based upon this study, we can recommend that these three proposed metrics can be considered sufficiently robust for the ecological status assessment of European lakes in WFD monitoring schemes.     

Trophic networks: How do theories link ecosystem structure and functioning to stability properties? A review

In the context of present global changes, interest in understanding how systems respond to anthropogenic environmental pressures and stress has increased. Indices that characterize ecosystem state are helpful tools for the interpretation of ecosystem responses. The central question is how to link these responses to ecosystem structure and functioning and to quantify ecosystem persistence, resistance or resilience. Quantification and characterization of trophic networks by ecological network analysis (ENA) indices is proceeding rapidly, especially in the field of coastal ecology. In this contribution, we review several theories that relate ecosystem structure and function to stability. The structure and functioning of ecosystems change during the maturation of ecosystems. In the first section, the maturation of ecosystems is described using thermodynamics. In the second and third parts of this paper, we define some concepts for analysing structure and functioning of food webs and discuss their relation to stability. In the last section, we describe three ENA indices and their link to stability. We demonstrate that ENA provides powerful tools for describing local stability, combining quantitative and qualitative concepts. However, it remains incomplete for describing real conservation cases that combine local and global stability.     

Are we selecting appropriate metrics to assess human impacts on biodiversity?

Biased and subjective choices of metrics to be used in ecological studies could lead researchers to reach misleading conclusions regarding patterns of biodiversity response to human disturbances. Nevertheless, little attention has been given to the choices of variables in the majority of studies published to date. Here, we used the literature concerning land use change effects on dung beetles to assess the extent to which variables commonly employed in ecological studies correspond to those deemed to be most important by researchers of the same studies. Specifically, we examined both biodiversity (response) and environmental (explanatory) metrics from a comprehensive literature review and compared their use with their relative importance, according to a survey of the authors of the studies. Our results highlight marked disparities between researchers opinion expressed in our survey and their choice of variables in published papers. We suggest that these disparities are due to the high costs of sampling and processing some variables, logistical constraints and different perceptions of importance amongst researchers. We highlight the importance of these issues for our understanding of the biodiversity consequences of land use change, and highlight some recommendations for alleviating this issue.     

The consequences of mass mortality events for the structure and dynamics of biological communities

Mass mortality events (MMEs) are rapidly occurring, substantial population losses that transpire within a short time interval relative to the generation time of the affected organism. Previous work has established that MMEs appear to be increasing in frequency and magnitude; however, currently, there is little understanding of the consequences of MMEs for biological communities. Here, we use theory and empirical data from observed MMEs to understand how MMEs impact the structure and dynamics of communities. To do so, we build upon existing resource pulse and trophic cascade theory to show that MMEs both share similarities and diverge from these ecological phenomena, producing distinct short‐ and long‐term impacts by jointly altering the effects of species interactions across trophic levels and providing an influx of resources from decaying biomass. Second, we investigate how the magnitude of MMEs, trophic level of the impacted species, overall food web structure and ecosystem type may mediate the resulting ecological response. Third, we compare the understanding gained by our models to existing observational data on MMEs. Our synthesis, offers an empirical path forward for understanding MMEs through experimentation and improved observational data collection. While complex, resolving the consequences of MMEs should be a high research priority due to their role in determining how ecological systems respond to environmental change driven by rare events.     

A causal examination of the effects of confounding factors on multimetric indices

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.