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Marine spatial population dynamics are often addressed with a focus on larval dispersal, without taking into account movement behavior of individuals in later life stages. Processes occurring during demersal life stages may also drive spatial population dynamics if habitat quality is perceived differently by animals belonging to different life stages. In this study, we used a dual approach to understand how stage‐structured habitat use and dispersal ability of adults shape the population of a marine fish species. Our study area and focal species provided us with the unique opportunity to study a closed island population. A spatial simulation model was used to estimate dispersal distances along a coral reef that surrounds the island, while contributions of different nursery bays were determined based on otolith stable isotope signatures of adult reef fish. The model showed that adult dispersal away from reef areas near nursery bays is limited. The results further show that different bays contributed unequally to the adult population on the coral reef, with productivity of juveniles in bay nursery habitat determining the degree of mixing among local populations on the reef and with one highly productive area contributing most to the island's reef fish population. The contribution of the coral reef as a nursery habitat was minimal, even though it had a much larger surface area. These findings indicate that the geographic distribution of nursery areas and their productivity are important drivers for the spatial distribution patterns of adults on coral reefs. We suggest that limited dispersal of adults on reefs can lead to a source–sink structure in the adult stage, where reefs close to nurseries replenish more isolated reef areas. Understanding these spatial population dynamics of the demersal phase of marine animals is of major importance for the design and placement of marine reserves, as nursery areas contribute differently to maintain adult populations.

The agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decision-making processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

Urban planning increasingly requires navigating complex socio‐spatial dynamics and uncertainties, particularly when addressing social challenges where stakeholders hold diverse perspectives and knowledge. This article introduces PaSyMo (Participatory Systems Modeling), a gamified communication support system designed to assist urban planners in communicative and deliberative planning. PaSyMo integrates three conceptual pillars that guided its design: stakeholder engagement, participatory agent‐based modeling (ABM), and visualization on tangible interfaces. The system combines a simulation environment grounded in geodata and ABM with discursive elements from scenario workshops and role‐playing games, bridging digital and non‐digital formats. PaSyMo contributes to the growing field of GAM research (Games and Agent‐based Modeling; Szczepanska et al., 2022) by providing a framework that explores the integration of gaming mechanics with urban simulation tools, highlighting their potential to support sustainable urban planning. The approach draws from participatory modeling (Sterling et al., 2019; Voinov & Bousquet, 2010) while leveraging state‐of‐the‐art geospatial simulation and interactive interfaces to facilitate communication and co‐production of knowledge among diverse stakeholders. Exploratory findings suggest that combining gaming experience with geospatial data visualization and ABM offers a promising approach to communicate the potential implications and trade‐offs of urban planning initiatives. This integration enhances stakeholder engagement, promotes shared understanding, and supports consensus building. By making urban planning processes more interactive, PaSyMo can extend the impact of planning research beyond academic settings. Preliminary insights indicate that PaSyMo can enhance stakeholder understanding, knowledge integration, consensus‐building, and proactive planning, especially in contexts of decision‐making in complex and uncertain situations; however, these findings need to be substantiated in future studies.

Habitat loss and population fragmentation pose severe threats to biodiversity and the survival of many species. Population isolation and the decline in effective population size lead to increased genetic drift and inbreeding. In turn, this reduces neutral diversity, and it also affects the genetic load of deleterious mutations. Here, we analyse the effect of such genomic erosion by designing a spatially explicit, individual based model in SLiM, simulating the effects of the recorded habitat loss in Mauritius over the past ~ 250 years. We show that the loss of neutral diversity (genome-wide heterozygosity) was barely noticeable during the first 100 years of habitat loss. Changes to the genetic load took even more time to register, and they only became apparent circa 200 years after the start of habitat decline. Although a considerable number of deleterious mutations were lost by drift, others increased in frequency. The masked load was thus converted into a realised load, which compromised individual fitness and population viability after much of the native habitat had been lost. Importantly, genomic erosion continued after the metapopulation had stabilised at low numbers. Our study shows that historic habitat loss can pose a sustained threat to populations also in future generations, even without further habitat loss. The UN’s Decade on Ecosystem Restoration needs to lead to transformative change to save species from future extinction, and this requires the urgent restoration of natural habitats.

Cumulative impacts assessments on marine ecosystems have been hindered by the difficulty of collecting environmental data and identifying drivers of community dynamics beyond local scales. On coral reefs, an additional challenge is to disentangle the relative influence of multiple drivers that operate at different stages of coral ontogeny. We integrated coral life history, population dynamics, and spatially explicit environmental drivers to assess the relative and cumulative impacts of multiple stressors across 2,300 km of the world’s largest coral reef ecosystem, Australia’s Great Barrier Reef (GBR). Using literature data, we characterized relationships between coral life history processes (reproduction, larval dispersal, recruitment, growth, and mortality) and environmental variables. We then simulated coral demographics and stressor impacts at the organism (coral colony) level on >3,800 individual reefs linked by larval connectivity and exposed to temporally and spatially realistic regimes of acute (crown-of-thorns starfish outbreaks, cyclones, and mass coral bleaching) and chronic (water-quality) stressors. Model simulations produced a credible reconstruction of recent (2008–2020) coral trajectories consistent with monitoring observations, while estimating the impacts of each stressor at reef and regional scales. Overall, simulated coral populations declined by one-third across the GBR, from an average of ~29% to ~19% hard coral cover. By 2020, <20% of the GBR had coral cover higher than 30%, a status of reef health corroborated by scarce and sparsely distributed monitoring data. Reef-wide annual rates of coral mortality were driven by bleaching (48%) ahead of cyclones (41%) and starfish predation (11%). Beyond the reconstructed status and trends, the model enabled the emergence of complex interactions that compound the effects of multiple stressors while promoting a mechanistic understanding of coral cover dynamics. Drivers of coral cover growth were identified; notably, water quality (suspended sediments) was estimated to delay recovery for at least 25% of inshore reefs. Standardized rates of coral loss and recovery allowed the integration of all cumulative impacts to determine the equilibrium cover for each reef. This metric, combined with maps of impacts, recovery potential, water-quality thresholds, and reef state metrics, facilitates strategic spatial planning and resilience-based management across the GBR.

BackgroundEuro–Mediterranean regions are expected to undergo a climate-induced exacerbation of fire activity in the upcoming decades. Reliable predictions of fire behaviour represent an essential instrument for planning and optimising fire management actions and strategies.AimsThe aim of this study was to describe and analyse the performance of an agent-based spatial simulation model for predicting wildland surface fire spread and growth.MethodsThe model integrates Rothermel’s equations to obtain fire spread metrics and uses a hybrid raster–vector implementation to predict patterns of fire growth. The model performance is evaluated in quantitative terms of spatiotemporal agreement between predicted patterns of fire growth and reference patterns, under both ideal and real-world environmental conditions, using case studies in Sardinia, Italy.Key resultsPredicted patterns of fire growth demonstrate negligible distortions under ideal conditions when compared with circular or elliptical reference patterns. In real-world heterogeneous conditions, a substantial agreement between observed and predicted patterns is achieved, resulting in a similarity coefficient of up to 0.76.ConclusionsOutcomes suggest that the model exhibits promising performance with low computational requirements.ImplicationsAssuming that parametric uncertainty is effectively managed and a rigorous validation encompassing additional case studies from Euro–Mediterranean regions is conducted, the model has the potential to provide a valuable contribution to operational fire management applications.

In the natural environment, bacteria most often live in communities bound to one another by secreted adhesives. These communities, or biofilms, play a central role in biogeochemical cycling, microbiome functioning, wastewater treatment, and disease. Wherever there are bacteria, there are also viruses that attack them, called phages. Interactions between bacteria and phages are likely to occur ubiquitously in biofilms. We show here, using simulations and experiments, that biofilms will in most conditions allow phage-susceptible bacteria to be protected from phage exposure, if they are growing alongside other cells that are phage resistant. This result has implications for the fundamental ecology of phage-bacteria interactions, as well as the development of phage-based antimicrobial therapeutics. Encounters among bacteria and their viral predators (bacteriophages) are among the most common ecological interactions on Earth. These encounters are likely to occur with regularity inside surface-bound communities that microbes most often occupy in natural environments. Such communities, termed biofilms, are spatially constrained: interactions become limited to near neighbors, diffusion of solutes and particulates can be reduced, and there is pronounced heterogeneity in nutrient access and physiological state. It is appreciated from prior theoretical work that phage-bacteria interactions are fundamentally different in spatially structured contexts, as opposed to well-mixed liquid culture. Spatially structured communities are predicted to promote the protection of susceptible host cells from phage exposure, and thus weaken selection for phage resistance. The details and generality of this prediction in realistic biofilm environments, however, are not known. Here, we explore phage-host interactions using experiments and simulations that are tuned to represent the essential elements of biofilm communities. Our simulations show that in biofilms, phage-resistant cells—as their relative abundance increases—can protect clusters of susceptible cells from phage exposure, promoting the coexistence of susceptible and phage-resistant bacteria under a large array of conditions. We characterize the population dynamics underlying this coexistence, and we show that coexistence is recapitulated in an experimental model of biofilm growth measured with confocal microscopy. Our results provide a clear view into the dynamics of phage resistance in biofilms with single-cell resolution of the underlying cell-virion interactions, linking the predictions of canonical theory to realistic models and in vitro experiments of biofilm growth. IMPORTANCE In the natural environment, bacteria most often live in communities bound to one another by secreted adhesives. These communities, or biofilms, play a central role in biogeochemical cycling, microbiome functioning, wastewater treatment, and disease. Wherever there are bacteria, there are also viruses that attack them, called phages. Interactions between bacteria and phages are likely to occur ubiquitously in biofilms. We show here, using simulations and experiments, that biofilms will in most conditions allow phage-susceptible bacteria to be protected from phage exposure, if they are growing alongside other cells that are phage resistant. This result has implications for the fundamental ecology of phage-bacteria interactions, as well as the development of phage-based antimicrobial therapeutics.

Urban areas in sub-Saharan Africa are facing significant developmental challenges due to rapid population growth and urban expansion, this study aims to predict urban growth and assess the SDG 11.3.1 indicator in the Chambishi multi-facility economic zone (CFEMZ) in Zambia through the integration of remote sensing data and spatial cooperative simulation so as to realize sustainable development goals (SDGs). The study utilized DMSP-OLS and VIIRS nighttime light data between 2000 and 2020 to extract the urban built-up area by applying the Pseudo-Invariant Features (PIFs) method to determine thresholds. The land-use and population changes under several development scenarios in 2030 were simulated in the study using the Spatial Cooperative Simulation (SCS) approach. The changes in SDG 11.3.1 indicators were also calculated in the form of a spatialized kilometer grid. The findings show a substantial rise in the built-up area and especially indicate a most notable increase in Chambishi. The primary cause of this growth is the development of industrial parks, which act as the region’s principal engine for urban expansion. Under the natural scenario, the land-use distribution in the study area presents an unplanned state that will make it difficult to realize SDGs. The results of the spatialization form of the SDG 11.3.1 indicator demonstrate the areas and problems of imbalance between urban construction and population growth in the CMFEZ. This study demonstrates the importance of remote sensing of nighttime lighting and spatial simulation in urban planning to achieve SDG 11.3.1 for sustainable urbanization in industrial cities.

In the context of accelerating global urbanization and sustainable development challenges, impervious surfaces, as a key component of urban land cover, are significantly associated with regional economic development. This study takes Harbin, a typical cold region city, as a research object and constructs a three-level analytical framework of “land surface classification-economic simulation-mechanism analysis.” By innovatively integrating multi-source remote sensing, demographic, and economic data, the research addresses gaps in understanding urban sustainability in cold environments. An enhanced XGBoost algorithm was employed to achieve high-precision classification of ten land surface materials, resulting in a high overall accuracy. Furthermore, a gridded GDP spatialization model developed using high-resolution population data demonstrated superior performance compared to traditional methods. Machine learning-assisted analysis revealed that asphalt and metal surfaces are the most significant impervious materials driving economic output, reflecting the respective influences of transportation infrastructure and industrial agglomeration. Spatial pattern analysis indicates that Harbin’s impervious surfaces exhibit a lower fractal dimension and a distinct grid-like morphology compared to the typical subtropical city of Guangzhou, underscoring urban form adaptations to cold climatic constraints. The strong spatial coupling between gradients of GDP intensity and the attenuation of impervious surface density is quantitatively confirmed. This study provides a quantitative basis and a transferable technical framework for optimizing land use intensity and infrastructure planning in cold cities, thereby offering a scientific foundation for sustainable, intensive land utilization in climate-vulnerable urban systems.

To reduce the spatial simulation error generated by the finite difference method, previous researchers compute the optimal finite-difference weights always by minimizing the error of spatial dispersion relation. However, we prove that the spatial simulation error of the finite difference method is associated with the dot product of the spatial dispersion relation of the finite-difference weights and the spectrum of the seismic wavefield. Based on the dot product relation, we construct a L2 norm cost function to minimize spatial simulation error. For solving this optimization problem, the seismic wavefield information in wavenumber region is necessary. Nevertheless, the seismic wavefield is generally obtained by costly forward modeling techniques. To reduce the computational cost, we substitute the spectrum of the seismic wavelet for the spectrum of the seismic wavefield, as the seismic wavelet plays a key role in determining the seismic wavefield. In solving the optimization problem, we design an exhaustive search method to obtain the solution of the L2 norm optimization problem. After solving the optimization problem, we are able to achieve the finite-difference weights that minimize spatial simulation error. In theoretical error analyses, the finite-difference weights from the proposed method can output more accurate simulation results compared to those from previous optimization algorithms. Furthermore, we validate our method through numerical tests with synthetic models, which encompass homogenous/inhomogeneous media as well as isotropic and anisotropic media.