Ecological complexity and the biosphere: the next 30 years

The effect of climate legacies on extinction dynamics: A systematic review

One of the main objectives of ecological research is to enhance our understanding of the processes that lead to species extinction. A potentially crucial extinction pattern is the dependence of contemporary biodiversity dynamics on past climates, also known as "climate legacy". However, the general impact of climate legacy on extinction dynamics is unknown. Here, we conduct a systematic review to summarize the effect of climate legacies on extinction dynamics. We find that few works studying the relationship between extinction dynamics and climate include the potential impact of climate legacies (10%), with even fewer studies reaching beyond merely discussing them (3%). Among the studies that quantified climate legacies, six out of seven reported an improved fit of models to extinction dynamics, with most also describing substantial impacts of legacy effects on extinction risk. These include an increase in extinction risk of up to 40% when temperature changes add to a long-term trend in the same direction, as well as substantial effects on species' adaptations, population dynamics and juvenile recruitment. Various ecological processes have been identified in the literature as potential ways in which climate legacies could affect the vulnerability of modern ecosystems to anthropogenic climate change, including niche conservatism, physiological thresholds, time lags and cascading effects. Overall, we find high agreement that climate legacy is a crucial process shaping extinction dynamics. Incorporating climate legacies in biodiversity assessments could be a key step toward a better understanding of the ecological consequences arising from climate change.

Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza

Motivation

Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales.

Results

We developed a KG to demonstrate the method's utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen-host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems.

Availability and implementation

The data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp.

The Anthropocene and the biodiversity crisis: an eco-evolutionary perspective

A major facet of the Anthropocene is global change, such as climate change, caused by human activities, which drastically affect biodiversity with all-scale declines and homogenization of biotas. This crisis does not only affect the ecological dynamics of biodiversity, but also its evolutionary dynamics, including genetic diversity, an aspect that is generally neglected. My tenet is therefore to consider biodiversity dynamics from an eco-evolutionary perspective, i.e. explicitly accounting for the possibility of rapid evolution and its feedback on ecological processes and the environment. I represent the impact of the various avatars of global change in a temporal perspective, from pre-industrial time to the near future, allowing to visualize their dynamics and to set desired values that should not be trespassed for a given time (e.g., +2 °C for 50 years from now). After presenting the impact of various stressors (e.g., climate change) on biodiversity, this representation is used to heuristically show the relevance of an eco-evolutionary perspective: (i) to analyze how biodiversity will respond to the stressors, for example by seeking out more suitable conditions or adapting to new conditions; (ii) to serve in predictive exercises to envision future dynamics (decades to centuries) under stressor impact; (iii) to propose nature-based solutions to the crisis. Significant obstacles stand in the way of the development of such an approach, in particular the general lack of interest in intraspecific diversity, and perhaps more generally a lack of understanding that, we, humans, are only a modest part of biodiversity.

CompLex: Legal systems through the lens of complexity science

While "complexity science" has achieved significant successes in several interdisciplinary fields such as economics and biology, it is only a very recent observation that legal systems -from the way legal texts are drafted and connected to the rest of the corpus, up to the level of how judges and courts reach decisions under a variety of conflicting inputs- share several features with standard Complex Adaptive Systems. This review is meant as a gentle introduction to the use of quantitative tools and techniques of complexity science to describe, analyse, and tame the complex web of human interactions that the Law is supposed to regulate. We offer an overview of the main directions of research undertaken so far as well as an outlook for future research, and we argue that statistical physicists and complexity scientists should not ignore the opportunities offered by the cross-fertilisation between legal scholarship and complex-systems modelling.

Synthetic Ecosystems: From the Test Tube to the Biosphere

The study of ecosystems, both natural and artificial, has historically been mediated by population dynamics theories. In this framework, quantifying population numbers and related variables (associated with metabolism or biological-environmental interactions) plays a central role in measuring and predicting system-level properties. As we move toward advanced technological engineering of cells and organisms, the possibility of bioengineering ecosystems (from the gut microbiome to wildlands) opens several questions that will require quantitative models to find answers. Here, we present a comprehensive survey of quantitative modeling approaches for managing three kinds of synthetic ecosystems, sharing the presence of engineered strains. These include test tube examples of ecosystems hosting a relatively low number of interacting species, mesoscale closed ecosystems (or ecospheres), and macro-scale, engineered ecosystems. The potential outcomes of synthetic ecosystem designs and their limits will be relevant to different disciplines, including biomedical engineering, astrobiology, space exploration and fighting climate change impacts on endangered ecosystems. We propose a space of possible ecosystems that captures this broad range of scenarios and a tentative roadmap for open problems and further exploration.

Cooperative control of environmental extremes by artificial intelligent agents

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging, given the potentially large number of individual and environmental variables involved. This article shows how to address this problem by using fire as the source of extreme events. We implement a simulated environment where fire propagates on a spatial landscape, and a group of artificial agents learn how to harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential artificial intelligence management of complex ecosystems are discussed.

Establishing complexity targets to enhance artificial reef designs

Artificial reefs (AR), which are integral tools for fish management, ecological reconciliation and restoration efforts, require non-polluting materials and intricate designs that mimic natural habitats. Despite their three-dimensional complexity, current designs nowadays rely on empirical methods that lack standardised pre-immersion assessment. To improve ecosystem integration, we propose to evaluate 3-dimensional Computer-aided Design (3D CAD) models using a method inspired by functional ecology principles. Based on existing metrics, we assess geometric (C-convexity, P-packing, D-fractal dimension) and informational complexity (R-specific richness, H- diversity, J-evenness). Applying these metrics to different reefs constructed for habitat protection, biomass production and bio-mimicry purposes, we identify potential complexity target points (CTPs). This method provides a framework for improving the effectiveness of artificial reef design by allowing for the adjustment of structural properties. These CTPs represent the first step in enhancing AR designs. We can refine them by evaluating complexity metrics derived from 3D reconstructions of natural habitats to advance bio-mimicry efforts. In situ, post-immersion studies can help make the CTPs more specific for certain species of interest by exploring complexity-diversity or complexity-species distribution relationships at the artificial reef scale.

Understanding diversity-synchrony-stability relationships in multitrophic communities

Understanding how species loss impacts ecosystem stability is critical given contemporary declines in global biodiversity. Despite decades of research on biodiversity-stability relationships, most studies are performed within a trophic level, overlooking the multitrophic complexity structuring natural communities. Here, in a global analysis of diversity-synchrony-stability (DSS) studies (n = 420), we found that 74% were monotrophic and biased towards terrestrial plant communities, with 91% describing stabilizing effects of asynchrony. Multitrophic studies (26%) were representative of all biomes and showed that synchrony had mixed effects on stability. To explore potential mechanisms, we applied a multitrophic framework adapted from DSS theory to investigate DSS relationships in algae-herbivore assemblages across five long-term tropical and temperate marine system datasets. Both algal and herbivore species diversity reduced within-group synchrony in both systems but had different interactive effects on species synchrony between systems. Herbivore synchrony was positively and negatively influenced by algal diversity in tropical versus temperate systems, respectively, and algal synchrony was positively influenced by herbivore diversity in temperate systems. While herbivore synchrony reduced multitrophic stability in both systems, algal synchrony only reduced stability in tropical systems. These results highlight the complexity of DSS relationships at the multitrophic level and emphasize why more multitrophic assessments are needed to better understand how biodiversity influences community stability in nature.

Biodiversity as a firewall to engineered microbiomes for restoration and conservation

The possibility of abrupt transitions threatens to poise ecosystems into irreversibly degraded states. Synthetic biology has recently been proposed to prevent them from crossing tipping points. However, there is little understanding of the impact of such intervention on the resident communities. Can such modification have 'unintended consequences', such as loss of species? Here, we address this problem by using a mathematical model that allows us to simulate this intervention scenario explicitly. We show how the indirect effect of damping the decay of shared resources results in biodiversity increase, and last but not least, the successful incorporation of the synthetic within the ecological network and very small-positive changes in the population size of the resident community. Furthermore, extensions and implications for future restoration and terraformation strategies are discussed.

Self-organization as a mechanism of resilience in dryland ecosystems

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.

Epistemology of synthetic biology: a new theoretical framework based on its potential objects and objectives

Synthetic biology is a new research field which attempts to understand, modify, and create new biological entities by adopting a modular and systemic conception of the living organisms. The development of synthetic biology has generated a pluralism of different approaches, bringing together a set of heterogeneous practices and conceptualizations from various disciplines, which can lead to confusion within the synthetic biology community as well as with other biological disciplines. I present in this manuscript an epistemological analysis of synthetic biology in order to better define this new discipline in terms of objects of study and specific objectives. First, I present and analyze the principal research projects developed at the foundation of synthetic biology, in order to establish an overview of the practices in this new emerging discipline. Then, I analyze an important scientometric study on synthetic biology to complete this overview. Afterwards, considering this analysis, I suggest a three-level classification of the object of study for synthetic biology (which are different kinds of living entities that can be built in the laboratory), based on three successive criteria: structural hierarchy, structural origin, functional origin. Finally, I propose three successively linked objectives in which synthetic biology can contribute (where the achievement of one objective led to the development of the other): interdisciplinarity collaboration (between natural, artificial, and theoretical sciences), knowledge of natural living entities (past, present, future, and alternative), pragmatic definition of the concept of "living" (that can be used by biologists in different contexts). Considering this new theoretical framework, based on its potential objects and objectives, I take the position that synthetic biology has not only the potential to develop its own new approach (which includes methods, objects, and objectives), distinct from other subdisciplines in biology, but also the ability to develop new knowledge on living entities.

Eco-evolution from deep time to contemporary dynamics: The role of timescales and rate modulators

Eco-evolutionary dynamics, or eco-evolution for short, are often thought to involve rapid demography (ecology) and equally rapid heritable phenotypic changes (evolution) leading to novel, emergent system behaviours. We argue that this focus on contemporary dynamics is too narrow: Eco-evolution should be extended, first, beyond pure demography to include all environmental dimensions and, second, to include slow eco-evolution which unfolds over thousands or millions of years. This extension allows us to conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the 'theatre' in which ecology and evolution are two interacting 'players'. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change. Our perspective attempts to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from today to deep time.

A time with e-Natureza (e-Nature): a model of nature-based health interventions as a complex adaptive system

Discussions surrounding the positive impacts of nature on human health and strategies to enhance our connection with the natural world have been ongoing. However, a limited number of theoretical models are available to guide research and practice in this area. Therefore, there is a pressing need for a systematic framework that outlines clear steps for conducting research implementing nature-based interventions. In this study, we investigate the relationship between health and nature through the lens of Complex Adaptive Systems. This approach involves examining the dynamic interactions between multiple interconnected elements to understand the complex emergent behaviors that arise from such relationships. Our model is designed to support nature-based interventions, considering the essential interdependence between humans and nature. This perspective aims to improve both human health and biodiversity conservation in a mutually beneficial manner. The underlying interactions that drive nature-based health interventions are thoroughly explored, leading us to propose a novel intervention model named "A time with e-Natureza" (e-Nature). This model encompasses four types of experiences, drawing from scientific literature and insights from authors engaged in an interdisciplinary research group: (1) Aesthetic and emotional experience; (2) Multisensory integration experience; (3) Knowledge experience; and (4) Engagement experience. Each experience within the model targets affective, cognitive, and behavioral aspects, with a specific focus on fostering a deeper connection with nature. Distinct activities are incorporated within each experience to promote successful outcomes. The model is grounded in existing theories that address the human-nature relationship and is informed by Nursing theories that support health promotion interventions. By presenting this new model, our aim is to contribute to the effective implementation of nature-based interventions that not only enhance human well-being but also support the conservation of nature. This integrated approach recognizes the mutual benefits of human-nature interaction and offers valuable insights for future research and practical applications in the fields of nature and health.

A framework for the detection and attribution of biodiversity change

The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of biodiversity change is needed. We propose an inferential framework to guide detection and attribution analyses, which identifies five steps-causal modelling, observation, estimation, detection and attribution-for robust attribution. This workflow provides evidence of biodiversity change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for trend detection and attribution have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. We illustrate these steps with examples. This framework could strengthen the bridge between biodiversity science and policy and support effective actions to halt biodiversity loss and the impacts this has on ecosystems. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Toward a cohesive understanding of ecological complexity

Ecological systems are quintessentially complex systems. Understanding and being able to predict phenomena typical of complex systems is, therefore, critical to progress in ecology and conservation amidst escalating global environmental change. However, myriad definitions of complexity and excessive reliance on conventional scientific approaches hamper conceptual advances and synthesis. Ecological complexity may be better understood by following the solid theoretical basis of complex system science (CSS). We review features of ecological systems described within CSS and conduct bibliometric and text mining analyses to characterize articles that refer to ecological complexity. Our analyses demonstrate that the study of complexity in ecology is a highly heterogeneous, global endeavor that is only weakly related to CSS. Current research trends are typically organized around basic theory, scaling, and macroecology. We leverage our review and the generalities identified in our analyses to suggest a more coherent and cohesive way forward in the study of complexity in ecology.

How can physiology best contribute to wildlife conservation in a warming world?

Global warming is now predicted to exceed 1.5°C by 2033 and 2°C by the end of the 21st century. This level of warming and the associated environmental variability are already increasing pressure on natural and human systems. Here we emphasize the role of physiology in the light of the latest assessment of climate warming by the Intergovernmental Panel on Climate Change. We describe how physiology can contribute to contemporary conservation programmes. We focus on thermal responses of animals, but we acknowledge that the impacts of climate change are much broader phylogenetically and environmentally. A physiological contribution would encompass environmental monitoring, coupled with measuring individual sensitivities to temperature change and upscaling these to ecosystem level. The latest version of the widely accepted Conservation Standards designed by the Conservation Measures Partnership includes several explicit climate change considerations. We argue that physiology has a unique role to play in addressing these considerations. Moreover, physiology can be incorporated by institutions and organizations that range from international bodies to national governments and to local communities, and in doing so, it brings a mechanistic approach to conservation and the management of biological resources.

Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms

Accurate estimation of evaporation is of great significance for understanding regional drought, and managing and applying limited water resources in dryland. However, the application of the traditional estimation approaches is limited due to the lack of required meteorological parameters or experimental conditions. In this study, a novel hybrid model was proposed to estimate the monthly pan Ep in dryland by integrating long short-term memory (LSTM) with grey wolf optimizer (GWO) algorithm and Kendall-τ correlation coefficient, where the GWO algorithm was employed to find the optimal hyper-parameters of LSTM, and Kendall-τ correlation coefficient was used to determine the input combination of meteorological variables. The model performance was compared to the performance of other methods based on the evaluation metrics, including root mean squared error (RMSE), the normalized mean squared error (NMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and Nash-Sutcliffe coefficient of efficiency (NSCE). The results indicated that the optimal input meteorological parameters of the hybrid Kendall-τ-GWO-LSTM models are the monthly average temperature, the minimum air temperature, the maximum air temperature, the minimum values of RMSE, NMSE, MAE, and MAPE are 38.28, 0.20, 26.62, and 19.96%, and the maximum NSCE is 0.89, suggesting that the hybrid Kendall-τ-GWO-LSTM exhibit better model performance than the other hybrid models. Thus, the hybrid Kendall-τ-GWO-LSTM model was highly recommended for estimating pan Ep with limited meteorological information in dryland. The present investigation provides a novel method to estimate the monthly pan Ep with limited meteorological variables in dryland by coupling a deep learning model with meta-heuristic algorithms and the data preprocessing techniques.

Stability of multi-layer ecosystems

Community structure is reported to play a critical role in ecosystem stability, which indicates the ability of a system to return to equilibrium after perturbations. However, current studies rely on the assumption that the community consists of only a single-layer structure. In practice, multi-layer structures are common in ecosystems, e.g. the distributions of both microorganisms in strata and fish in the ocean usually stratify into multi-layer structures. Here we use multi-layer networks to model species interactions within each layer and between different layers, and study the stability of multi-layer ecosystems under different interaction types. We show that competitive interactions within each layer have a more substantial stabilizing effect in multi-layer ecosystems relative to their impact in single-layer ecosystems. Surprisingly, between different layers, we find that competition between species destabilizes the ecosystem. We further provide a theoretical analysis of the stability of multi-layer ecosystems and confirm the robustness of our findings for different connectances between layers, numbers of species in each layer, and numbers of layers. Our work provides a general framework for investigating the stability of multi-layer ecosystems and uncovers the double-sided role of competitive interactions in the stability of multi-layer ecosystems.