Mechanistic modeling of climate effects on redistribution and population growth in a community of fish species

Understanding community responses to climate is critical for anticipating the future impacts of global change. However, despite increased research efforts in this field, models that explicitly include important biological mechanisms are lacking. Quantifying the potential impacts of climate change on species is complicated by the fact that the effects of climate variation may manifest at several points in the biological process. To this end, we extend a dynamic mechanistic model that combines population dynamics, such as species interactions, with species redistribution by allowing climate to affect both processes. We examine their relative contributions in an application to the changing biomass of a community of eight species in the Gulf of Maine using over 30 years of fisheries data from the Northeast Fishery Science Center. Our model suggests that the mechanisms driving biomass trends vary across space, time, and species. Phase space plots demonstrate that failing to account for the dynamic nature of the environmental and biologic system can yield theoretical estimates of population abundances that are not observed in empirical data. The stock assessments used by fisheries managers to set fishing targets and allocate quotas often ignore environmental effects. At the same time, research examining the effects of climate change on fish has largely focused on redistribution. Frameworks that combine multiple biological reactions to climate change are particularly necessary for marine researchers. This work is just one approach to modeling the complexity of natural systems and highlights the need to incorporate multiple and possibly interacting biological processes in future models.     

Combining field experiments and individual-based modeling to identify the dynamically relevant organizational scale in a field system

Community ecologists continually strive to build analytical models that realistically describe long‐term dynamics of the systems they study. A key step in this process is identifying which details are relevant for predicting dynamics. Currently, this remains a limiting step in development of analytical theory because experimental field ecology, which provides the key empirical insight, and theoretical ecology, which translates empirical knowledge into analytical theory, remain weakly linked. I illustrate how an individual‐based computational model of species interactions is a useful way to bridge the gulf between empirical research and theory development. I built a computational model that reproduced key natural history and biological detail of an old‐field interaction web composed of a predator species, a herbivore species and two plant groups that had been the subject of extensive previous field research. I examined, using simulation experiments, how individual behavior of herbivores in response to changing resource and predator abundance scaled to long‐term population‐level and community‐level dynamics. The simulation experiments revealed that the long‐term community dynamics could be highly predictable because of two counterintuitive reasons. First, seasonality was a strong forcing variable on the system that removed the possibility of serial dependence in population abundance over time. Second, because of seasonality, short‐term behavioral responses of herbivores played a much stronger role in shaping community structure than longer‐term processes such as density responses. So, simply knowing the short‐term responses of herbivores at the evolutionary ecological level was sufficient to forecast the long‐term outcome of experimental manipulations. This study shows that an individual‐based model, once it is calibrated to the real‐world field system, can provide key insight into the biological detail that analytical models should include to predict long‐term dynamics.     

Applying landscape metrics to species distribution model predictions to characterize internal range structure and associated changes

Distributional shifts in species ranges provide critical evidence of ecological responses to climate change. Assessments of climate-driven changes typically focus on broad-scale range shifts (e.g. poleward or upward), with ecological consequences at regional and local scales commonly overlooked. While these changes are informative for species presenting continuous geographic ranges, many species have discontinuous distributions—both natural (e.g. mountain or coastal species) or human-induced (e.g. species inhabiting fragmented landscapes)—where within-range changes can be significant. Here, we use an ecosystem engineer species (Sabellaria alveolata) with a naturally fragmented distribution as a case study to assess climate-driven changes in within-range occupancy across its entire global distribution. To this end, we applied landscape ecology metrics to outputs from species distribution modelling (SDM) in a novel unified framework. SDM predicted a 27.5% overall increase in the area of potentially suitable habitat under RCP 4.5 by 2050, which taken in isolation would have led to the classification of the species as a climate change winner. SDM further revealed that the latitudinal range is predicted to shrink because of decreased habitat suitability in the equatorward part of the range, not compensated by a poleward expansion. The use of landscape ecology metrics provided additional insights by identifying regions that are predicted to become increasingly fragmented in the future, potentially increasing extirpation risk by jeopardising metapopulation dynamics. This increased range fragmentation could have dramatic consequences for ecosystem structure and functioning. Importantly, the proposed framework—which brings together SDM and landscape metrics—can be widely used to study currently overlooked climate-driven changes in species internal range structure, without requiring detailed empirical knowledge of the modelled species. This approach represents an important advancement beyond predictive envelope approaches and could reveal itself as paramount for managers whose spatial scale of action usually ranges from local to regional.     

Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling

Aim Robust and reliable predictions of the effects of climate change on biodiversity are required in formulating conservation and management strategies that best retain biodiversity into the future. Significant challenges in modelling climate change impacts arise from limitations in our current knowledge of biodiversity. Community‐level modelling can complement species‐level approaches in overcoming these limitations and predicting climate change impacts on biodiversity as a whole. However, the community‐level approaches applied to date have been largely correlative, ignoring the key processes that influence change in biodiversity over space and time. Here, we suggest that the development of new ‘semi‐mechanistic’ community‐level models would substantially increase our capacity to predict climate change impacts on biodiversity. Location Global. Methods Drawing on an expansive review of biodiversity modelling approaches and recent advances in semi‐mechanistic modelling at the species level, we outline the main elements of a new semi‐mechanistic community‐level modelling approach. Results Our quantitative review revealed a sharp divide between mechanistic and non‐mechanistic biodiversity modelling approaches, with very few semi‐mechanistic models developed to date. Main conclusions We suggest that the conceptual framework presented here for combining mechanistic and non‐mechanistic community‐level approaches offers a promising means of incorporating key processes into predictions of climate change impacts on biodiversity whilst working within the limits of our current knowledge.     

Cascading extinctions, functional complementarity, and selection in two-trophic-level model communities: A trait-based mechanistic approach

The influence of diversity on ecosystem functioning and ecosystem services is now well established. Yet predictive mechanistic models that link species traits and community-level processes remain scarce, particularly for multitrophic systems. Here we revisit MacArthur's classical consumer resource model and develop a trait-based approach to predict the effects of consumer diversity on cascading extinctions and aggregated ecosystem processes in a two-trophic-level system. We show that functionally redundant efficient consumers generate top-down cascading extinctions. This counterintuitive result reveals the limits of the functional redundancy concept to predict the consequences of species deletion. Our model also predicts that the biodiversity-ecosystem functioning relationship is different for different ecosystem processes and depends on the range of variation of consumer traits in the regional species pool, which determines the sign of selection effects. Lastly, competition among resources and consumer generalism both weaken complementarity effects, which suggests that selection effects may prevail at higher trophic levels. Our work emphasizes the potential of trait-based approaches for transforming biodiversity and ecosystem functioning research into a more predictive science.     

The macroecology of infectious diseases: a new perspective on global‐scale drivers of pathogen distributions and impacts

Identifying drivers of infectious disease patterns and impacts at the broadest scales of organisation is one of the most crucial challenges for modern science, yet answers to many fundamental questions remain elusive. These include what factors commonly facilitate transmission of pathogens to novel host species, what drives variation in immune investment among host species, and more generally what drives global patterns of parasite diversity and distribution? Here we consider how the perspectives and tools of macroecology, a field that investigates patterns and processes at broad spatial, temporal and taxonomic scales, are expanding scientific understanding of global infectious disease ecology. In particular, emerging approaches are providing new insights about scaling properties across all living taxa, and new strategies for mapping pathogen biodiversity and infection risk. Ultimately, macroecology is establishing a framework to more accurately predict global patterns of infectious disease distribution and emergence.     

Managing consequences of climate‐driven species redistribution requires integration of ecology,conservation and social science

Climate change is driving a pervasive global redistribution of the planet's species. Species redistribution poses new questions for the study of ecosystems, conservation science and human societies that require a coordinated and integrated approach. Here we review recent progress, key gaps and strategic directions in this nascent research area, emphasising emerging themes in species redistribution biology, the importance of understanding underlying drivers and the need to anticipate novel outcomes of changes in species ranges. We highlight that species redistribution has manifest implications across multiple temporal and spatial scales and from genes to ecosystems. Understanding range shifts from ecological, physiological, genetic and biogeographical perspectives is essential for informing changing paradigms in conservation science and for designing conservation strategies that incorporate changing population connectivity and advance adaptation to climate change. Species redistributions present challenges for human well‐being, environmental management and sustainable development. By synthesising recent approaches, theories and tools, our review establishes an interdisciplinary foundation for the development of future research on species redistribution. Specifically, we demonstrate how ecological, conservation and social research on species redistribution can best be achieved by working across disciplinary boundaries to develop and implement solutions to climate change challenges. Future studies should therefore integrate existing and complementary scientific frameworks while incorporating social science and human‐centred approaches. Finally, we emphasise that the best science will not be useful unless more scientists engage with managers, policy makers and the public to develop responsible and socially acceptable options for the global challenges arising from species redistributions.     

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed 'omics' technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A 'system' approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with 'system approaches' in animal sciences, providing exciting opportunities to predict and modulate animal traits.     

Neuroecology and the need for broader synthesis

Neuroecology combines physiological and ecological principles toward understanding behavioral mechanisms and their roles in establishing patterns of organismal abundances and species distributions. This amalgamation of research approaches incorporates the strengths of neuroethology to determine the cellular basis of behavior. It, however, treads where neuroethology does not by establishing critical linkages between neural processes and the population- and community-level consequences of individual behavior. Neuroecology also promotes understanding of nervous systems within a strong environmental context by encouraging use of keystone and foundation species as critical "ecological models" for studies of electrically excitable cells. Previous investigations of environmental stress, metabolism, and energy relations have proven the value of a combined cellular biochemical and biophysical approach toward predicting natural patterns of organismal abundances and species distributions. Borrowing from this approach, neuroecology would coalesce neuroscience with population and community ecology to establish how individual behavior functions, and how such behavior acts to determine higher-order biological processes.     

Can species data only be appropriately used to conserve biodiversity?

Despite widely acknowledged handicaps of the species approach to identifying priority conservation areas, many workers continue to use these flawed techniques as the backbone of their analyses. Species-based approaches address only a small part of biological diversity by ignoring different levels of organisation as well as the functional linkages among these levels. These data are often biased and incomplete and are often used in preference to data dealing with higher biological levels of organisation though the latter may be readily available. Within the framework of Noss's [(1990) Conservation Biology 4: 355–364] hierarchical definition of biodiversity (and Scott etal. [(1993) Wildlife Monographs 123: 1–31] gap analysis), we propose a top-down model dealing with broad organisational levels first, and finer-scale species distributions last. Note that we do not discard the latter approach, but merely argue for its use at a stage when, in our opinion, it adds most to the value of the prioritisation exercise. The model is flexible so that additional information, particularly those related to threats to biological diversity, can be added when they are available.     

玛曲高寒沼泽化草甸51种植物光合生理和叶片形态特征的比较

基于功能性状的研究方法广泛地应用于生态学研究, 用于解释不同层次的复杂的生态学过程, 而绿色植物叶片的功能性状长期被认为对植物的生存、生长和繁殖具有重要的影响。该研究对玛曲高寒沼泽化草甸51个植物种(分属于14科)的叶片形态和光合性状进行测量, 比较不同物种和不同功能群(莎草科、禾本科和双子叶类杂草)的差异, 分析叶片形态特征和叶片光合性状之间的相关性。结果表明: 1)不同物种、不同功能群之间在比叶面积、净光合速率和水分利用效率等叶片形态和光合特征方面有着显著的差异, 例如禾本科植物具有较高的比叶面积和水分利用效率, 双子叶类杂草具有较大的叶面积, 而莎草科植物具有较高的净光合速率。2)相关性分析结果显示, 无论在物种水平还是功能群水平, 叶片形态和叶片光合性状之间都具有显著的相关关系。该研究揭示了高寒沼泽化草甸植物物种在叶片功能性状上的显著分化, 进而使得这些物种能在同一个草地群落中共存, 而群落中不同功能群物种的组成差异将会对群落的结构、功能和资源利用产生显著的影响。该研究将为进一步研究高寒沼泽化草甸提供基础研究数据并为其保护和恢复提供生理生态学依据。     

The ecological impacts of nighttime light pollution: a mechanistic appraisal

The ecological impacts of nighttime light pollution have been a longstanding source of concern, accentuated by realized and projected growth in electrical lighting. As human communities and lighting technologies develop, artificial light increasingly modifies natural light regimes by encroaching on dark refuges in space, in time, and across wavelengths. A wide variety of ecological implications of artificial light have been identified. However, the primary research to date is largely focused on the disruptive influence of nighttime light on higher vertebrates, and while comprehensive reviews have been compiled along taxonomic lines and within specific research domains, the subject is in need of synthesis within a common mechanistic framework. Here we propose such a framework that focuses on the cross‐factoring of the ways in which artificial lighting alters natural light regimes (spatially, temporally, and spectrally), and the ways in which light influences biological systems, particularly the distinction between light as a resource and light as an information source. We review the evidence for each of the combinations of this cross‐factoring. As artificial lighting alters natural patterns of light in space, time and across wavelengths, natural patterns of resource use and information flows may be disrupted, with downstream effects to the structure and function of ecosystems. This review highlights: (i) the potential influence of nighttime lighting at all levels of biological organisation (from cell to ecosystem); (ii) the significant impact that even low levels of nighttime light pollution can have; and (iii) the existence of major research gaps, particularly in terms of the impacts of light at population and ecosystem levels, identification of intensity thresholds, and the spatial extent of impacts in the vicinity of artificial lights.     

Linking genotypes to phenotypes and fitness: how mechanistic biology can inform molecular ecology

The accessibility of new genomic resources, high‐throughput molecular technologies and analytical approaches such as genome scans have made finding genes contributing to fitness variation in natural populations an increasingly feasible task. Once candidate genes are identified, we argue that it is necessary to take a mechanistic approach and work up through the levels of biological organization to fully understand the impacts of genetic variation at these candidate genes. We demonstrate how this approach provides testable hypotheses about the causal links among levels of biological organization, and assists in designing relevant experiments to test the effects of genetic variation on phenotype, whole‐organism performance capabilities and fitness. We review some of the research programs that have incorporated mechanistic approaches when examining naturally occurring genetic and phenotypic variation and use these examples to highlight the value of developing a comprehensive understanding of the relationship between genotype and fitness. We give suggestions to guide future research aimed at uncovering and understanding the genetic basis of adaptation and argue that further integration of mechanistic approaches will help molecular ecologists better understand the evolution of natural populations.     

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand, there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.     

Multiscale modelling of chromatin organisation: Resolving nucleosomes at near-atomistic resolution inside genes

The three-dimensional organisation of the genome modulates biological processes and is, in turn, transformed by the activity in the nucleus. Not surprisingly, understanding how the genome operates requires uncovering the fundamental biophysical and molecular mechanisms that establish and regulate its organisation. Genome organisation starts with the formation of chromatin: a polymer of nucleoprotein complexes, termed nucleosomes, that carry variable chemical signatures according to their biological context. The physicochemical heterogeneity of chromatin, the stochastic organisation it fosters, and the multiscale nature of genome organisation pose great technical challenges. Excitingly, advances in imaging and molecular biology techniques are addressing chromatin organisation at increasing resolutions. In tandem, computer models are testing and postulating hypotheses, interpreting the experimental data, and linking molecular properties of nucleosomes to the mesoscale organisation of chromatin. We discuss how coarse-grained models at varying resolutions are expanding our mechanistic understanding of chromatin organisation, and the challenges still remaining in the field.     

Trends in research on shallow water food webs

Making sense of the spider–web networks of interactions between species in food webs has been a major pre-occupation of ecologists over the last 60 years. This review describes the early attempts to reduce this complexity through the grouping of individual taxa into functional categories (such as trophic levels), through adopting the energy flow or systems approach as epitomised by the International Biological Programme, and most recently by the derivation of web statistics by food web theorists. The strengths and weaknesses of these approaches are discussed in relation to empirical field experiments for unravelling the processes responsible for organising communities and an assessment made of the representation of these approaches in the marine biological literature.