Predictive ecology: systems approaches

The world is experiencing significant, largely anthropogenically induced, environmental change. This will impact on the biological world and we need to be able to forecast its effects. In order to produce such forecasts, ecology needs to become more predictive--to develop the ability to understand how ecological systems will behave in future, changed, conditions. Further development of process-based models is required to allow such predictions to be made. Critical to the development of such models will be achieving a balance between the brute-force approach that naively attempts to include everything, and over simplification that throws out important heterogeneities at various levels. Central to this will be the recognition that individuals are the elementary particles of all ecological systems. As such it will be necessary to understand the effect of evolution on ecological systems, particularly when exposed to environmental change. However, insights from evolutionary biology will help the development of models even when data may be sparse. Process-based models are more common, and are used for forecasting, in other disciplines, e.g. climatology and molecular systems biology. Tools and techniques developed in these endeavours can be appropriated into ecological modelling, but it will also be necessary to develop the science of ecoinformatics along with approaches specific to ecological problems. The impetus for this effort should come from the demand coming from society to understand the effects of environmental change on the world and what might be performed to mitigate or adapt to them.     

Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long‐term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat‐induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long‐term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range – with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot‐spells, in driving species–climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.     

Dynamic simulation of regulatory networks using SQUAD

Background  

The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology.     

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

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

Forecasting species range dynamics with process‐explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process–explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process–explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs – regulatory planning, extinction risk, climate refugia and invasive species – we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process‐explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.     

Climate change and coastal waterbird populations – past declines and future impacts

Considerable attention has been given to the impact of climate change on avian populations over the last decade. In this paper we examine two issues with respect to coastal bird populations in the UK: (1) is there any evidence that current populations are declining due to climate change, and (2) how might we predict the response of populations in the future? We review the cause of population decline in two species associated with saltmarsh habitats. The abundance of Common Redshank Tringa totanus breeding on saltmarsh declined by about 23% between the mid-1980s and mid-1990s, but the decline appears to have been caused by an increase in grazing pressure. The number of Twite Carduelis flavirostris wintering on the coast of East Anglia has declined dramatically over recent decades; there is evidence linking this decline with habitat loss but a causal role for climate change is unclear. These examples illustrate that climate change could be having population-level impacts now, but also show that it is dangerous to become too narrowly focused on single issues affecting coastal birds. Making predictions about how populations might respond to future climate change depends on an adequate understanding of important ecological processes at an appropriate spatial scale. We illustrate this with recent work conducted on the Icelandic population of Black-tailed Godwits Limosa limosa islandica that shows large-scale regulatory processes. Most predictive models to date have focused on local populations (single estuary or a group of neighbouring estuaries). We discuss the role such models might play in risk assessment, and the need for them to be linked to larger-scale ecological processes. We argue that future work needs to focus on spatial scale issues and on linking physical models of coastal environments with important ecological processes.     

Quantitative Trait Evolution and Environmental Change

Background

Given the recent changes in climate, there is an urgent need to understand the evolutionary ability of populations to respond to these changes.

Methodology/Principal Findings

We performed individual-based simulations with different shapes of the fitness curve, different heritabilities, different levels of density compensation, and different autocorrelation of environmental noise imposed on an environmental trend to study the ability of a population to adapt to changing conditions. The main finding is that when there is a positive autocorrelation of environmental noise, the outcome of the evolutionary process is much more unpredictable compared to when the noise has no autocorrelation. In addition, we found that strong selection resulted in a higher load, and more extinctions, and that this was most pronounced when heritability was low. The level of density-compensation was important in determining the variance in load when there was strong selection, and when genetic variance was lower when the level of density-compensation was low.

Conclusions

The strong effect of the details of the environmental fluctuations makes predictions concerning the evolutionary future of populations very hard to make. In addition, to be able to make good predictions we need information on heritability, fitness functions and levels of density compensation. The results strongly suggest that patterns of environmental noise must be incorporated in future models of environmental change, such as global warming.     

A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes

The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiquitous multipurpose analytical technique in biopharmaceutical manufacturing for real-time predictions of critical parameters in cell culture processes. The accuracy of Raman spectroscopy relies on chemometric models that need to be carefully calibrated. The existing calibration procedure is nontrivial to implement as it necessitates executing multiple carefully designed experiments for generating relevant calibration sets. Further, existing procedure yields calibration models that are reliable only in operating conditions they were calibrated in. This creates a unique challenge in clinical manufacturing where products have limited production history. In this paper, a novel machine-learning procedure based on just-in-time learning (JITL) is proposed to calibrate Raman models. Unlike traditional techniques, JITL-based generic Raman models can be reliably used for different modalities, cell lines, culture media, and operating conditions. The accuracy of JITL-based generic models is demonstrated on several validation studies involving real-time predictions of critical cell culture performance parameters, such as glucose, glutamate, glutamine, ammonium, lactate, sodium, calcium, viability, and viable cell density. The proposed JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, which to the best of authors’ knowledge have not been done before.     

The evolution of social interactions changes predictions about interacting phenotypes

In many traits involved in social interactions, such as courtship and aggression, the phenotype is an outcome of interactions between individuals. Such traits whose expression in an individual is partly determined by the phenotype of its social partner are called "interacting phenotypes." Quantitative genetic models suggested that interacting phenotypes can evolve much faster than nonsocial traits. Current models, however, consider the interaction between phenotypes of social partners as a fixed phenotypic response rule, represented by an interaction coefficient (ψ). Here, we extend existing theoretical models and incorporate the interaction coefficient as a trait that can evolve. We find that the evolution of the interaction coefficient can change qualitatively the predictions about the rate and direction of evolution of interacting phenotypes. We argue that it is crucial to determine whether and how the phenotypic response of an individual to its social partner can evolve to make accurate predictions about the evolution of traits involved in social interactions.     

Sex, Symbiosis and Coral Reef Communities

SYNOPSIS. Questions about how today's corals and coral reefswill fare in a future that holds not only increasing directanthropogenic impacts, but also global change, cannot be satisfactorilyanswered if we do not understand the relations of corals andreef systems to today's environmental conditions. This paperdiscusses four aspects of modern reef biology: coral reproduction,coral population biology, the coral-zooxanthella symbiosis,and reef community ecology. Conclusions of this survey of currentknowledge are that complexities of cnidarian reproductive biology,and our rudimentary knowledge of reproductive patterns in reefcnidarians, make forecasting based on current knowledge uncertainat best; new discoveries about the coral algal symbiotic systemsuggest a possible mode of adjustment to environmental changethat warrants a strong research effort; coral communities ofthe future may well be unlike what we are familiar with today;and these new assemblages will be shaped by the interactionof novel environmental conditions and the characteristics ofindividual reef species.     

Matching the forecast horizon with the relevant spatial and temporal processes and data sources

Most phenomenological, statistical models used to generate ecological forecasts take either a time-series approach, based on long-term data from one location, or a space-for-time approach, based on data describing spatial patterns across environmental gradients. However, the magnitude and even the sign of environment–response relationships detected using these two approaches often differs, leading to contrasting predictions about responses to future environmental change. Here we consider how the forecast horizon determines whether more accurate predictions come from the time-series approach, the space-for-time approach or a combination of the two. As proof of concept, we use simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. First, we simulated population or community dynamics under stationary temperature using two simple, mechanistic models. Second, we fit statistical models to the simulated data using a time-series approach, a space-for-time approach or a weighted average. We then forecast the response to a temperature increase using the statistical models, and compared these forecasts to temperature effects simulated by the mechanistic models. We found that the time-series approach made accurate short-term predictions because it captured initial conditions and effects of fast processes such as birth and death. The space-for-time approach made more accurate long-term predictions because it better captured the influence of slower processes such as evolutionary and ecological selection. The weighted average made accurate predictions at all time scales, including intermediate time-scales where the other two approaches performed poorly. A weighted average of time-series and space-for-time approaches shows promise, but making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.     

Modelling the effect of habitat fragmentation on range expansion in a butterfly

There is an increasing need for conservation programmes to make quantitative predictions of biodiversity responses to changed environments. Such predictions will be particularly important to promote species recovery in fragmented landscapes, and to understand and facilitate distribution responses to climate change. Here, we model expansion rates of a test species (a rare butterfly, Hesperia comma) in five landscapes over 18 years (generations), using a metapopulation model (the incidence function model). Expansion rates increased with the area, quality and proximity of habitat patches available for colonization, with predicted expansion rates closely matching observed rates in test landscapes. Habitat fragmentation constrained expansion, but in a predictable way, suggesting that it will prove feasible both to understand variation in expansion rates and to develop conservation programmes to increase rates of range expansion in such species.     

It Takes All Kinds in Acoustic Communication: A New Perspective on the Song Overlapping Phenomenon

Over the last few decades, research into song overlapping produced many – often conflicting – interpretations of its function and culminated in the current debate about the usefulness of this concept. To avoid a deadlock in song overlapping research, we present a new approach to existing evidence and offer several novel hypotheses that might help enhance future experiments. Our analysis offers both a theoretical perspective and specific predictions of each testable hypothesis. We present a detailed analysis of important questions. First, what information does song overlapping convey (is it a signal of aggressive intent or of male quality)? Second, what evolutionary mechanism stabilizes honesty of song overlapping as a signal (is it an index signal, handicap, proximity risk, conventional signal or a modifier)? Additionally, we offer some alternative explanations of the phenomenon (song overlapping as a mask or an incidental phenomenon). We hope to encourage future researchers not only to gather high‐quality experimental data, but also to make more careful interpretations, as we believe that no all‐encompassing explanation of song overlapping will be formulated any time soon. Focused comparative approaches will be necessary, as song overlapping might have different functions in different species.     

Impact: Toward a Framework for Understanding the Ecological Effects of Invaders

Although ecologists commonly talk about the impacts of nonindigenous species, little formal attention has been given to defining what we mean by impact, or connecting ecological theory with particular measures of impact. The resulting lack of generalizations regarding invasion impacts is more than an academic problem; we need to be able to distinguish invaders with minor effects from those with large effects in order to prioritize management efforts. This paper focuses on defining, evaluating, and comparing a variety of measures of impact drawn from empirical examples and theoretical reasoning. We begin by arguing that the total impact of an invader includes three fundamental dimensions: range, abundance, and the per-capita or per-biomass effect of the invader. Then we summarize previous approaches to measuring impact at different organizational levels, and suggest some new approaches. Reviewing mathematical models of impact, we argue that theoretical studies using community assembly models could act as a basis for better empirical studies and monitoring programs, as well as provide a clearer understanding of the relationship among different types of impact. We then discuss some of the particular challenges that come from the need to prioritize invasive species in a management or policy context. We end with recommendations about how the field of invasion biology might proceed in order to build a general framework for understanding and predicting impacts. In particular, we advocate studies designed to explore the correlations among different measures: Are the results of complex multivariate methods adequately captured by simple composite metrics such as species richness? How well are impacts on native populations correlated with impacts on ecosystem functions? Are there useful bioindicators for invasion impacts? To what extent does the impact of an invasive species depend on the system in which it is measured? Three approaches would provide new insights in this line of inquiry: (1) studies that measure impacts at multiple scales and multiple levels of organization, (2) studies that synthesize currently available data on different response variables, and (3) models designed to guide empirical work and explore generalities.     

A comparison between data requirements and availability for calibrating predictive ecological models for lowland UK woodlands: learning new tricks from old trees

Woodlands provide valuable ecosystem services, and it is important to understand their dynamics. To predict the way in which these might change, we need process‐based predictive ecological models, but these are necessarily very data intensive. We tested the ability of existing datasets to provide the parameters necessary to instantiate a well‐used forest model (SORTIE) for a well‐studied woodland (Wytham Woods). Only five of SORTIE's 16 equations describing different aspects of the life history and behavior of individual trees could be parameterized without additional data collection. One age class – seedlings – was completely missed as they are shorter than the height at which Diameter at Breast Height (DBH) is measured. The mensuration of trees has changed little in the last 400 years (focussing almost exclusively on DBH) despite major changes in the nature of the source of value obtained from trees over this time. This results in there being insufficient data to parameterize process‐based models in order to meet the societal demand for ecological prediction. We do not advocate ceasing the measurement of DBH, but we do recommend that those concerned with tree mensuration consider whether additional measures of trees could be added to their data collection protocols. We also see advantages in integrating techniques such as ground‐based LIDAR or remote sensing techniques with long‐term datasets to both preserve continuity with what has been performed in the past and to expand the range of measurements made.     

Changes in the distribution of multispecies pest assemblages affect levels of crop damage in warming tropical Andes

Climate induced species range shifts might create novel interactions among species that may outweigh direct climatic effects. In an agricultural context, climate change might alter the intensity of competition or facilitation interactions among pests with, potentially, negative consequences on the levels of damage to crop. This could threaten the productivity of agricultural systems and have negative impacts on food security, but has yet been poorly considered in studies. In this contribution, we constructed and evaluated process‐based species distribution models for three invasive potato pests in the Tropical Andean Region. These three species have been found to co‐occur and interact within the same potato tuber, causing different levels of damage to crop. Our models allowed us to predict the current and future distribution of the species and therefore, to assess how damage to crop might change in the future due to novel interactions. In general, our study revealed the main challenges related to distribution modeling of invasive pests in highly heterogeneous regions. It yielded different results for the three species, both in terms of accuracy and distribution, with one species surviving best at lower altitudes and the other two performing better at higher altitudes. As to future distributions our results suggested that the three species will show different responses to climate change, with one of them expanding to higher altitudes, another contracting its range and the other shifting its distribution to higher altitudes. These changes will result in novel areas of co‐occurrence and hence, interactions of the pests, which will cause different levels of damage to crop. Combining population dynamics and species distribution models that incorporate interspecific trade‐off relationships in different environments revealed a powerful approach to provide predictions about the response of an assemblage of interacting species to future environmental changes and their impact on process rates.     

Using functional morphology to examine the ecology and evolution of specialization

Researchers strive to understand what makes species different,and what allows them to survive in the time and space that theydo. Many models have been advanced which encompass an arrayof ecological, evolutionary, mathematical, and logical principles.The goal has been to develop ecological theories that can, amongother things, make specific and robust predictions about howand where organisms should live and what organisms should utilize.The role of functional morphology is often an under-appreciatedparameter of these models. A more complete understanding ofhow anatomical features work to allow the organism to accomplishcertain tasks has allowed us to revisit some of these ideaswith a new perspective. We illustrate our view of this rolefor functional morphology in ecology by considering the issueof specialization: we attempt to align several definitions ofspecialization based upon shared ecological and evolutionaryprinciples, and we summarize theoretical predictions regardingwhy an organism might specialize. Kinematic studies of preycapture in several types of fishes are explored with regardto the potential ecological and evolutionary consequences ofspecialization, most notably in the area of trade-offs. We suggestthat a functional morphological perspective can increase ourunderstanding of the ecological concepts of specialization andit consequences. The kinds of data that functional morphologistscollect can help us to quantify organismal performance associatedwith specialization and the union of functional morphology withecology can help us to better understand not just how but whyorganisms interact in the manner that they do.     

US forest response to projected climate‐related stress: a tolerance perspective

Although it is widely recognized that climate change will require a major spatial reorganization of forests, our ability to predict exactly how and where forest characteristics and distributions will change has been rather limited. Current efforts to predict future distribution of forested ecosystems as a function of climate include species distribution models (for fine‐scale predictions) and potential vegetation climate envelope models (for coarse‐grained, large‐scale predictions). Here, we develop and apply an intermediate approach wherein we use stand‐level tolerances of environmental stressors to understand forest distributions and vulnerabilities to anticipated climate change. In contrast to other existing models, this approach can be applied at a continental scale while maintaining a direct link to ecologically relevant, climate‐related stressors. We first demonstrate that shade, drought, and waterlogging tolerances of forest stands are strongly correlated with climate and edaphic conditions in the conterminous United States. This discovery allows the development of a tolerance distribution model (TDM), a novel quantitative tool to assess landscape level impacts of climate change. We then focus on evaluating the implications of the drought TDM. Using an ensemble of 17 climate change models to drive this TDM, we estimate that 18% of US ecosystems are vulnerable to drought‐related stress over the coming century. Vulnerable areas include mostly the Midwest United States and Northeast United States, as well as high‐elevation areas of the Rocky Mountains. We also infer stress incurred by shifting climate should create an opening for the establishment of forest types not currently seen in the conterminous United States.     

Cetacean range and climate in the eastern North Atlantic: future predictions and implications for conservation

There is increasing evidence that the distributions of a large number of species are shifting with global climate change as they track changing surface temperatures that define their thermal niche. Modelling efforts to predict species distributions under future climates have increased with concern about the overall impact of these distribution shifts on species ecology, and especially where barriers to dispersal exist. Here we apply a bio‐climatic envelope modelling technique to investigate the impacts of climate change on the geographic range of ten cetacean species in the eastern North Atlantic and to assess how such modelling can be used to inform conservation and management. The modelling process integrates elements of a species' habitat and thermal niche, and employs “hindcasting” of historical distribution changes in order to verify the accuracy of the modelled relationship between temperature and species range. If this ability is not verified, there is a risk that inappropriate or inaccurate models will be used to make future predictions of species distributions. Of the ten species investigated, we found that while the models for nine could successfully explain current spatial distribution, only four had a good ability to predict distribution changes over time in response to changes in water temperature. Applied to future climate scenarios, the four species‐specific models with good predictive abilities indicated range expansion in one species and range contraction in three others, including the potential loss of up to 80% of suitable white‐beaked dolphin habitat. Model predictions allow identification of affected areas and the likely time‐scales over which impacts will occur. Thus, this work provides important information on both our ability to predict how individual species will respond to future climate change and the applicability of predictive distribution models as a tool to help construct viable conservation and management strategies.     

Individual‐based ecology of coastal birds

Conservation objectives for non‐breeding coastal birds (shorebirds and wildfowl) are determined from their population size at coastal sites. To advise coastal managers, models must predict quantitatively the effects of environmental change on population size or the demographic rates (mortality and reproduction) that determine it. As habitat association models and depletion models are not able to do this, we developed an approach that has produced such predictions thereby enabling policy makers to make evidence‐based decisions. Our conceptual framework is individual‐based ecology, in which populations are viewed as having properties (e.g. size) that arise from the traits (e.g. behaviour, physiology) and interactions of their constituent individuals. The link between individuals and populations is made through individual‐based models (IBMs) that follow the fitness‐maximising decisions of individuals and predict population‐level consequences (e.g. mortality rate) from the fates of these individuals. Our first IBM was for oystercatchers Haematopus ostralegus and accurately predicted their density‐dependent mortality. Subsequently, IBMs were developed for several shorebird and wildfowl species at several European sites, and were shown to predict accurately overwinter mortality, and the foraging behaviour from which predictions are derived. They have been used to predict the effect on survival in coastal birds of sea level rise, habitat loss, wind farm development, shellfishing and human disturbance. This review emphasises the wider applicability of the approach, and identifies other systems to which it could be applied. We view the IBM approach as a very useful contribution to the general problem of how to advance ecology to the point where we can routinely make meaningful predictions of how populations respond to environmental change.