Complex Ecological Systems Research Articles

Generating spatially realistic environmental null models with the shift‐&‐rotate approach helps evaluate false positives in species distribution modelling

Abstract To circumvent reporting spurious correlations, species distribution models often explicitly account for spatial autocorrelation, for example by including spatially structured random effects. The validity of statistical inference derived from such models has been tested by simulations using null environmental predictors that do not have any causal dependency with the response. Such null environmental predictors can be obtained by permutations of the original predictors or by simulating spatial structures resembling the original predictors. In such approaches, it is important that the permuted or simulated predictors reflect the nature of spatial variation present in the original predictors. Here we present a novel approach for generating realistic null predictors by a shift‐&‐rotate (S&R) approach: we extract environmental variables after randomly translating and rotating the sampling area within a window of defined environmental layers. In this way, the null environmental variables have fully realistic spatial variation and covariation, but no relationship to the response variable. We implement the S&R approach to three main R‐functions and demonstrate with a simulation study how they can be used to untangle causal versus non‐causal relationships within species distribution modelling. These methods allow us to quantify the predictive power attributed within the models due to non‐causal correlations generated by the realistic structure of the environmental covariates. In our case study, we identify when a model incorrectly estimates parameter values, yet still has high predictive power due to the structured nature of the predictor variables. The use of null models is imperative in ecological modelling for testing the accuracy of statistical inference in complex ecological systems and the choice of these null models is far from trivial. Here we provide R functions for generating spatially realistic null models to use in species distribution modelling as well as other spatially explicit fields such as landscape genetics.

Accurately uncovering the regional ecological restoration priorities: A multidimensional approach to eco-dynamic sustainable management

Accurate identification, projection and dynamic evaluation of ecological degradation priorities are important prerequisites for realizing ecological restoration and territorial protection. However, the complexity of ecological systems and environmental diversity often limit the effectiveness of single-model evaluation approaches. Utilizing Badong County as a case study, this paper integrated the InVEST-Circuit-PLUS model to comprehensively analyze the eco-dynamic sustainability and restoration needs. Through performing projection of land cover, construction of ecological source areas, calculation of ecological resistance characteristics and extraction of ecological elements, we were able to identify priority ecological restoration areas over a twenty-year period (2007–2027). The results revealed cyclical land cover transformations from forest to cropland and back to forest, with an overall trend of forest cover expansion. While the county's ecological quality showed a slight decline, the location and extent of high-value ecological source areas were mapped in detail. By integrating ecosystem service value assessments, driver factor analyses, and the development of a "One Belt, Two Axes, Three Sources and Four Zones" ecological management framework, we were able to uncover the nuanced spatial and temporal dynamics shaping the regional ecology. These findings provide an evidence-based foundation to guide targeted ecological restoration and sustainable land use planning, which has verified the feasibility of the framework.

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

Computing macroscopic reaction rates in reaction-diffusion systems using Monte Carlo simulations.

Stochastic reaction-diffusion models are employed to represent many complex physical, biological, societal, and ecological systems. The macroscopic reaction rates describing the large-scale, long-time kinetics in such systems are effective, scale-dependent renormalized parameters that need to be either measured experimentally or computed by means of a microscopic model. In a Monte Carlo simulation of stochastic reaction-diffusion systems, microscopic probabilities for specific events to happen serve as the input control parameters. To match the results of any computer simulation to observations or experiments carried out on the macroscale, a mapping is required between the microscopic probabilities that define the Monte Carlo algorithm and the macroscopic reaction rates that are experimentally measured. Finding the functional dependence of emergent macroscopic rates on the microscopic probabilities (subject to specific rules of interaction) is a very difficult problem, and there is currently no systematic, accurate analytical way to achieve this goal. Therefore, we introduce a straightforward numerical method of using lattice Monte Carlo simulations to evaluate the macroscopic reaction rates by directly obtaining the count statistics of how many events occur per simulation time step. Our technique is first tested on well-understood fundamental examples, namely, restricted birth processes, diffusion-limited two-particle coagulation, and two-species pair annihilation kinetics. Next we utilize the thus gained experience to investigate how the microscopic algorithmic probabilities become coarse-grained into effective macroscopic rates in more complex model systems such as the Lotka-Volterra model for predator-prey competition and coexistence, as well as the rock-paper-scissors or cyclic Lotka-Volterra model and its May-Leonard variant that capture population dynamics with cyclic dominance motifs. Thereby we achieve a more thorough and deeper understanding of coarse graining in spatially extended stochastic reaction-diffusion systems and the nontrivial relationships between the associated microscopic and macroscopic model parameters, with a focus on ecological systems. The proposed technique should generally provide a useful means to better fit Monte Carlo simulation results to experimental or observational data.

Exploring cooperative hunting dynamics and PRCC analysis: insights from a spatio-temporal mathematical model

The proposed mathematical model explores the intricate dynamics of a predator-prey system involving prey infection and cooperative hunting of predators. The model incorporates habitat complexity, emphasizing its influence on ecological interactions. The well-posedness of the system has rigorously been examined in a temporal setting and also conducted stability analysis. The bifurcation analysis reveals the existence of several local bifurcations on the system, namely transcritical bifurcation, saddle-node bifurcation, and Hopf bifurcation. Furthermore, these investigations delineate the two-dimensional bifurcations including Bogdanov–Takens and cusp bifurcations for different parametric combinations. With suitable choices of parameter values, the proposed model exhibits diverse dynamic phenomena, including bistable and tri-stable behavior. Latin hypercube sampling is utilized to conduct uncertainty analysis on input parameters, aiming to observe their effects on population dynamics. Subsequently, Kendall’s tau and Spearman’s rank correlation coefficients are also computed to investigate the impact of these uncertainties on the population. In the later part, a spatio-temporal system is proposed with two-dimensional diffusion terms to obtain the conditions for Turing instability. Numerical simulations have been conducted to observe the emergence of spatial patterns and the impact of predator cooperation in these patterns. The study provides valuable insights into the dynamics of complex ecological systems, emphasizing the interplay of spatial and temporal factors in shaping population dynamics and predator-prey interactions.

Environmental Politics and Policy in Aotearoa New Zealand

With an ensemble of experts, Maria Bargh and Julie L. MacArthur have done a timely service to the communities of academic research, teaching-and-learning, and public policymaking by editing the book Environmental Politics and Policy in Aotearoa New Zealand. It is timely as we witness another year of record-breaking dramatic weather and climate-related disasters worldwide. More than ever, all sectors of society at large are compelled to learn more about the environment we live in, the histories of the lands, waters, and forests around us, interconnections across complex geological, ecological, and socio-economical systems, and, ultimately, the relationship between individuals and the future of Aotearoa and beyond.

Networking nutrients: How nutrition determines the structure of ecological networks.

Nutrients can shape ecological interactions but remain poorly integrated into ecological networks. Concepts such as nutrient-specific foraging nevertheless have the potential to expose the mechanisms structuring complex ecological systems. Nutrients also present an opportunity to predict dynamic processes, such as interaction rewiring and extinction cascades, and increase the accuracy of network analyses. Here, we propose the concept of nutritional networks. By integrating nutritional data into ecological networks, we envisage significant advances to our understanding of ecological processes from individual to ecosystem scales. We show that networks can be constructed with nutritional data to illuminate how nutrients structure ecological interactions in natural systems through an empirical example. Throughout, we identify fundamental ecological hypotheses that can be explored in a nutritional network context, alongside methods for resolving those networks. Nutrients influence the structure and complexity of ecological networks through mechanistic processes and concepts including nutritional niche differentiation, functional responses, landscape diversity, ecological invasions and ecosystem robustness. Future research on ecological networks should consider nutrients when investigating the drivers of network structure and function.

Interactions Between Serotonin Transporter Gene and Adverse Childhood Experience in a Generalized Additive Model: A Pilot Study.

While most people experience potentially traumatic events (PTEs), including Adverse Childhood Experiences (ACEs), the stress reactions to PTEs on mental health outcomes are highly heterogeneous. Resilience is influenced by a complex biopsychosocial ecological system, including gene serotonin transporter-linked promoter region or 5-HTTLPR /rs25531 by ACEs interactions. This pilot study investigated the gene-by-environment interactions on mental health outcomes in adults enrolled in a health care profession program using a generalized additive model (GAM). Seventy health care college students (mean age = 27.4 years, 67.1% women) participated in this cross-sectional study. Saliva samples were collected from students to analyze 5-HTTLPR/rs25531. Participants completed the ACE Questionnaire and the Mental Health Inventory. GAMs with different interaction terms were built adjusting for age, gender, and race. The value of the effective degree of freedom (EDF) quantifies the curvature of the relationship. Among participants with the long allele of 5-HTTLPR/rs25531, a linear pattern was found between the total ACE score and mental health outcomes (EDF = 1). Conversely, among participants with the short allele, EDF was approximately 2, indicating a curved association suggesting that mental health worsens in individuals exposed to up to four types of ACEs. The impact of up to four ACEs on mental health was stronger among individuals with the short allele of 5-HTTLPR/rs25531 than those with the long allele. Although this study does not claim to provide a definite approach to analyzing gene-by-environment interactions, we offer a different perspective to explore the relationship.

How flexible parasites can outsmart their hosts for evolutionary dominance

Antagonistic coevolution between hosts and parasites substantially impacts community structure, with parasites displaying fluctuating selection or arms race dynamics during coevolution. The traditional matching alleles (MA) and gene-for-gene (GFG) models have been used to describe the dynamics and interaction of host-parasite coevolution, with these models assuming that parasites adopt a single strategy when competing with other parasites. We present a nonlinear dynamic population model that challenges this assumption, showing how a parasite that is disadvantaged under either the MA or the GFG model can win the competition by switching between the two losing strategies based on an external environmental cue, internal processes, or stochastic decision-making. This counterintuitive outcome is analogous to Parrondo's paradox, a game-theoretic concept that shows how alternating between two losing strategies can result in a winning outcome. Our numerical experiments support the validity of this model, suggesting that parasites can greatly benefit from maximum flexibility in their interactions with hosts. The flexibility of successful parasites puts an extra burden on the host defenses that have to adapt to different strategies of the parasites. These findings contribute to a deeper understanding of the coevolution of parasites and hosts, with broad implications for the evolution of complex ecological systems. Published by the American Physical Society 2024

Understanding learners’ relationships with music

Relationships with music are at the core of music education. However, they are rarely studied from learners viewpoints—especially those of exceptionally motivated, advanced students—as they are incorporated into the theoretical underpinning or methodological stance of research in instrumental education. In this research project we follow the musical lives of ten advanced, mastery-oriented adolescent instrumentalists. The focus of this first report of our narrative ethnography is on how their relationships with music manifest in written narratives on the role of music and instrument learning in their life. While corroborating findings of previous research, the results demonstrate many idiosyncracies in the trajectories of our participants, including critical events. The results show that studying relationships with music and their development may reveal complex ecological systems in instrument learning and interconnected theoretical concepts, such as self-regulation, self-efficacy, agency, autonomy, identity, and metacognition. These phenomena—which can be challenging to differentiate, adapt, and apply in terms of learning practices—may seem far-removed from everyday musical practices, but can be consolidated when looking at relationships with music as gateways to the learning of musical instruments.

Diagnosis of the Integrated Coastal Zone Management of Peru

The marine-coastal zone is the region where marine and terrestrial elements come together, featuring complex ecological systems and human communities. The Integrated Coastal Management (icm) is a participatory process for the sustainable management of these areas, taking into consideration ecological, social, cultural, and economic aspects. The objective of this publication is to assess Peru’s icm, using the methodology proposed by Barragán (2003, 2005 y 2014), to evaluate administrative and legal elements at the three levels of government in coastal areas. Keywords: Marine-coastal zone, Integrated Coastal Management, Peru.

Rationalization, privatization and sustainability: debates and reforms involving management of the Lombard forests in the 18th century

This work examines the changes that took place in the management of forest commons in eighteenth-century Austrian Lombardy. During that period, fiscal and administrative reforms facilitated greater knowledge of the territory and the relationships between communities and wood merchants-entrepreneurs. Conflicting views arose when the Viennese government, aware that vast forest resources were being badly managed, took inspiration from modern theories of political economy and decided to privatize all forest commons. However, after careful research some enlightened Milanese officials began to consider mountain forests as part of a complex ecological system and question the environmental sustainability of the policies implemented.

Complex systems in ecology: a guided tour with large Lotka–Volterra models and random matrices

Ecosystems represent archetypal complex dynamical systems, often modelled by coupled differential equations of the form d x i d t = x i ϕ i ( x 1 , … , x N ) , where N represents the number of species and x i , the abundance of species i . Among these families of coupled differential equations, Lotka–Volterra (LV) equations, corresponding to ϕ i ( x 1 , … , x N ) = r i − x i + ( Γ x ) i , play a privileged role, as the LV model represents an acceptable trade-off between complexity and tractability. Here, r i is the intrinsic growth of species i and Γ stands for the interaction matrix: Γ i j represents the effect of species j over species i . For large N , estimating matrix Γ is often an overwhelming task and an alternative is to draw Γ at random, parameterizing its statistical distribution by a limited number of model features. Dealing with large random matrices, we naturally rely on random matrix theory (RMT). The aim of this review article is to present an overview of the work at the junction of theoretical ecology and large RMT. It is intended to an interdisciplinary audience spanning theoretical ecology, complex systems, statistical physics and mathematical biology.

Virtual Worlds for Learning in Metaverse: A Narrative Review

As digital technologies continue to evolve, they offer unprecedented opportunities to transform traditional educational paradigms. Virtual worlds offer a dynamic and immersive platform for fostering sustainability education, bridging the gap between theoretical knowledge and practical application. In these interactive environments, students can engage with complex ecological systems and sustainability challenges in a risk-free setting, allowing for experimentation and exploration that would be impractical or impossible in the real world. This study aims to investigate the application of various types of virtual worlds in educational settings, examine their characteristics and potential, and explore how they foster critical 21st-century skills like critical thinking, creativity, communication, and collaboration. This paper comprehensively explores various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—assessing their impact on educational processes and outcomes. Adventure Worlds, with narrative-driven quests, engage students in exploratory learning within a story context. Simulation Worlds replicate real-world environments, allowing students to practice and hone practical skills in a risk-free setting. Creative Worlds provide open-ended, sandbox-like environments where innovation and imagination are paramount. Role-Playing Worlds facilitate empathy and perspective-taking through character-driven scenarios, while Collaborative Worlds emphasize teamwork and problem-solving in group projects. The narrative review methodology was adopted for the comprehensive analysis and synthesis of the literature to assess the impact and integration of virtual worlds in education, focusing on identifying trends, challenges, and opportunities within this domain. The evaluation methodology used in this study incorporates a mix of the Theory of Inventive Problem Solving (TRIZ), Concept-Knowledge (C-K) theory, Structure-behavior-function (SBF) modeling, the Framework for 21st Century Learning (P21), and Universal Design for Learning (UDL) to evaluate the characteristics and educational potential of different virtual world types. Findings indicate that virtual worlds effectively support critical thinking, creativity, communication, and collaboration skills, presenting a comprehensive analysis of how these environments can support, supplement, or transform traditional educational models. The main outcome of the study is the comprehensive exploration of various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—in education, demonstrating their significant potential to enhance learning experiences and outcomes through immersive, interactive environments that foster critical thinking, creativity, communication, and collaboration skills.

Assessing a potential conflict associated with the production of Moringa oleifera in the Limpopo Province of South Africa: A systems thinking approach

The increased movement of humans throughout the world allowed the transportation of several species, such as Moringa oleifera Lam. (moringa), into biomes far away from their native habitation. Native to India, moringa is a versatile, drought-tolerant, and fast-growing tree that is easily adaptable to wide-ranging tropical and sub-tropical conditions around the world. It is used in cosmetics, as food and medicine for humans, livestock feed, crop biostimulant, and green manure. Even though moringa is an alien species to South Africa, its production is increasing, and its numerous uses are recognised by communities. Moringa forms part of a highly complex (social, ecological, and economic) system. This is because it is on the Species Under Surveillance for Possible Eradication or Containment Targets (SUSPECT) list under the National Environmental Management Biodiversity Act (NEM:BA) of South Africa. Listing species that are regarded as beneficial to communities on national regulations can cause conflicts and uncertainties among various stakeholders (i.e., environmental policymakers, farmers, rural communities, and government bodies). In this paper, a systems thinking approach was applied to address complex and conflicting issues linked to the production and overall status (economic, ecological, legal, and social) of moringa in South Africa. The Causal Loop Diagram (CLD) was developed to present a broad insight into the complexity of moringa in South Africa and assist in underscoring the feedback mechanisms within the system. Moreover, the CLD indicated that the position of moringa within the country comprised a variety of interdependent variables of government policies, environment, and society, which are interconnected into a multifaceted system. The potential conflict dimensions and types associated with allocating moringa an impact category within the South African context were identified, and this may serve as a useful tool for facilitating engagements and decision-making processes among stakeholders in resolving the status of moringa in South Africa.

Safety and efficacy of fecal microbiota transplantation (FMT) as a modern adjuvant therapy in various diseases and disorders: a comprehensive literature review.

The human gastrointestinal (GI) tract microbiome is a complex and all-encompassing ecological system of trillions of microorganisms. It plays a vital role in digestion, disease prevention, and overall health. When this delicate balance is disrupted, it can lead to various health issues. Fecal microbiota transplantation (FMT) is an emerging therapeutic intervention used as an adjuvant therapy for many diseases, particularly those with dysbiosis as their underlying cause. Its goal is to restore this balance by transferring fecal material from healthy donors to the recipients. FMT has an impressive reported cure rate between 80% and 90% and has become a favored treatment for many diseases. While FMT may have generally mild to moderate transient adverse effects, rare severe complications underscore the importance of rigorous donor screening and standardized administration. FMT has enormous potential as a practical therapeutic approach; however, additional research is required to further determine its potential for clinical utilization, as well as its safety and efficiency in different patient populations. This comprehensive literature review offers increased confidence in the safety and effectiveness of FMT for several diseases affecting the intestines and other systems, including diabetes, obesity, inflammatory and autoimmune illness, and other conditions.

Machine Learning Approaches for Pest and Insect Management in Forest Scenario: An Outlook

Forests are nature's most efficient complex ecological system and vulnerable resources of valuable products which contribute to the sustainable development of communities. In the current scenario of climate change, forest became susceptible to major issues such as diseases, insects, pests and their unpredictable pest outbreaks. The sustainable management and protection of this natural environment from insect, pest, diseases, human interference and unwanted disturbances is vital and needs new tools to find insight and effective management. Computer vision is good at spotting disorder and efficient pre-requisite tool for insect pest management. Hence, introduction of Artificial Intelligence (AI) and Machine Learning (ML) techniques could be an alternate advanced precision approach to detect and control the herbivorous insect pests at an early stage to avoid huge damage to forest and continuous indiscriminate usage of the chemical pesticides. This is an opportunity which benefits farmers, state forest departments, Forest Development Corporation, forest and private nurseries, wood-based industries, paper and pulp industries etc.

Light pollution in complex ecological systems.

Light pollution has emerged as a burgeoning area of scientific interest, receiving increasing attention in recent years. The resulting body of literature has revealed a diverse array of species-specific and context-dependent responses to artificial light at night (ALAN). Because predicting and generalizing community-level effects is difficult, our current comprehension of the ecological impacts of light pollution on complex ecological systems remains notably limited. It is critical to better understand ALAN's effects at higher levels of ecological organization in order to comprehend and mitigate the repercussions of ALAN on ecosystem functioning and stability amidst ongoing global change. This theme issue seeks to explore the effects of light pollution on complex ecological systems, by bridging various realms and scaling up from individual processes and functions to communities and networks. Through this integrated approach, this collection aims to shed light on the intricate interplay between light pollution, ecological dynamics and humans in a world increasingly impacted by anthropogenic lighting. This article is part of the theme issue 'Light pollution in complex ecological systems'.

Artificial light at night (ALAN) causes shifts in soil communities and functions.

Artificial light at night (ALAN) is increasing worldwide, but its effects on the soil system have not yet been investigated. We tested the influence of experimental manipulation of ALAN on two taxa of soil communities (microorganisms and soil nematodes) and three aspects of soil functioning (soil basal respiration, soil microbial biomass and carbon use efficiency) over four and a half months in a highly controlled Ecotron facility. We show that during peak plant biomass, increasing ALAN reduced plant biomass and was also associated with decreased soil water content. This further reduced soil respiration under high ALAN at peak plant biomass, but microbial communities maintained stable biomass across different levels of ALAN and times, demonstrating higher microbial carbon use efficiency under high ALAN. While ALAN did not affect microbial community structure, the abundance of plant-feeding nematodes increased and there was homogenization of nematode communities under higher levels of ALAN, indicating that soil communities may be more vulnerable to additional disturbances at high ALAN. In summary, the effects of ALAN reach into the soil system by altering soil communities and ecosystem functions, and these effects are mediated by changes in plant productivity and soil water content at peak plant biomass. This article is part of the theme issue 'Light pollution in complex ecological systems'.

Spectral composition of light-emitting diodes impacts aquatic and terrestrial invertebrate communities with potential implications for cross-ecosystem subsidies.

Resource exchanges in the form of invertebrate fluxes are a key component of aquatic-terrestrial habitat coupling, but this interface is susceptible to human activities, including the imposition of artificial light at night. To better understand the effects of spectral composition of light-emitting diodes (LEDs)-a technology that is rapidly supplanting other lighting types-on emergent aquatic insects and terrestrial insects, we experimentally added LED fixtures that emit different light spectra to the littoral zone and adjacent riparian habitat of a pond. We installed four replicate LED treatments of different wavelengths (410, 530 and 630 nm), neutral white (4000 k) and a dark control, and sampled invertebrates in both terrestrial and over-water littoral traps. Invertebrate communities differed among light treatments and between habitats, as did total insect biomass and mean individual insect size. Proportional allochthonous biomass was greater in the riparian habitat and among some light treatments, demonstrating an asymmetrical effect of differently coloured LEDs on aquatic-terrestrial resource exchanges. Overall, our findings demonstrate that variation in wavelength from LEDs may impact the flux of resources between systems, as well as the communities of insects that are attracted to particular spectra of LED lighting, with probable implications for consumers. This article is part of the theme issue 'Light pollution in complex ecological systems'.