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With the desire to model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright–Fisher model has been found to pose an obstacle to implementing many types of models; it is difficult to adapt the Wright–Fisher model, with its many assumptions, to modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright–Fisher or “nonWF” model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example models to illustrate their use.

Understanding the causes of spatial variation of soil carbon (C) has important implications for regional and global C dynamics studies. Soil C predictive models can identify sources of C variation, but may be influenced by scale parameters, including the spatial extent and resolution of input data. Our objective was to investigate the influence of these scale parameters on soil C spatial predictive models in Florida, USA. We used data from three nested spatial extents (Florida, 150,000 km2; Santa Fe River watershed, 3,585 km2; and University of Florida Beef Cattle Station, 5.58 km2) to derive stepwise linear models of soil C as a function of 24 environmental properties. Models were derived within the three extents and for seven resolutions (30–1920 m) of input environmental data in Florida and in the watershed, then cross‐evaluated among extents and resolutions, respectively. The quality of soil C models increased with an increase in the spatial extent (R2 from 0.10 in the cattle station to 0.61 in Florida) and with a decrease in the resolution of input data (R2 from 0.33 at 1920‐m resolution to 0.61 at 30‐m resolution in Florida). Soil and hydrologic variables were the most important across the seven resolutions both in Florida and in the watershed. The spatial extent and resolution of environmental covariates modulate soil C variation and soil‐landscape correlations influencing soil C predictive models. Our results provide scale boundaries to observe environmental data and assess soil C spatial patterns, supporting C sequestration, budgeting and monitoring programs. Key Points Soil carbon models are scale‐dependent Soil carbon models improve when spatial extents are increased Soil carbon models degrade as resolution of input data increases

Landscapes are closely linked to human well-being, but they are undergoing rapid and fundamental change. Understanding the societal transformation underlying these landscape changes, as well as the ecological and societal outcomes of landscape transformations across scales are prime areas for landscape research. We review and synthesize findings from six important areas of landscape research in Europe and discuss how these findings may advance the study of ecosystem change and society and its thematic key priorities. These six areas are: (1) linkages between people and the environment in landscapes, (2) landscape structure and land-use intensity, (3) long-term landscape history, (4) driving forces, processes, and actors of landscape change, (5) landscape values and meanings, and (6) landscape stewardship. We propose that these knowledge areas can contribute to the study of ecosystem change and society, considering nested multiscale dynamics of social-ecological systems; the stewardship of these systems and their ecosystem services; and the relationships between ecosystem services, human well-being, wealth, and poverty. Our synthesis highlights that knowledge about past and current landscape patterns, processes, and dynamics provides guidance for developing visions to support the sustainable stewardship of social-ecological systems under future conditions.

ContextThere have been many studies using species distribution models (SDMs) to predict shifts in species distributions due to environmental changes, but few consider effects of data quantity, data quality, or species response shape. Modeling studies using field-sampled data may be impaired to an unknown degree by lack of knowledge on species’ true relationships with environmental changes.ObjectivesUsing simulations with known relationships we assess model predictions, and investigate which models are more sensitive to sample size, detection limit, or species response shape issues when different SDMs are used for predicting species distribution shifts under environmental changes.MethodsWe simulated 16 species response relationships to ecological gradients differing in response shape (skewness and kurtosis) using a generalized β-function. Populations were randomly sampled at different sample sizes and detection limits. Linear discriminant analysis (LDA), multiple logistic regression (MLR), generalized additive models (GAM), boosted regression trees (BRT), random forests (RF), artificial neural networks (ANN), and maximum entropy models (MaxEnt) were developed on sampled datasets and compared for predicting species occurrence. We used these SDMs to predict distribution patterns for virtual species with different response shapes across a real landscape of varying heterogeneity in environmental conditions, and compared them with the probability of occurrence generated by the β-function.ResultsGAM and BRT were sensitive to both sample size and detection limit changes; RF was more affected by detection limit; ANN and MaxEnt were more affected by sample size; LDA and MLR were sensitive to species response shape changes.ConclusionsOverall, if little is known about species response to environmental changes, ANN is recommended especially for large sample size. If a focal species is likely to occur only in a narrow range of environmental conditions, GAM and BRT are preferred for large good-quality datasets, and GAM tends to perform slightly better under varied data conditions; RF is recommended for limited amounts of good-quality data. If a focal species is likely to be present in a wide range of environmental conditions, MaxEnt is preferred but caution should be taken for small sample size. If the goal is to identify potential distributions of invasive or endangered species but data quantity and quality are very limited, LDA and MLR are recommended as they generally provide reasonable model sensitivity.

Forests are projected to undergo dramatic compositional and structural shifts prompted by global changes, such as climatic changes and intensifying natural disturbance regimes. Future uncertainty makes planning for forest management exceptionally difficult, demanding novel approaches to maintain or improve the ability of forest ecosystems to respond and rapidly reorganize after disturbance events. Adopting a landscape perspective in forest management is particularly important in fragmented forest landscapes where both diversity and connectivity play key roles in determining resilience to global change. In this context, network analysis and functional traits combined with ecological dynamic modeling can help evaluate changes in functional response diversity and connectivity within and among forest stands in fragmented landscapes. Here, we coupled ecological dynamic modeling with functional traits analysis and network theory to analyze forested landscapes as an interconnected network of forest patches. We simulated future forest landscape dynamics in a large landscape in southern Quebec, Canada, under a combination of climate, disturbance, and management scenarios. We depicted the landscape as a functional network, assessed changes in future resilience using indicators at multiple spatial scales, and evaluated if current management practices are suitable for maintaining resilience to simulated changes in regimes. Our results show that climate change would promote forest productivity and favor heat-adapted deciduous species. Changes in natural disturbances will likely have negative impacts on native conifers and will drive changes in forest type composition. Climate change negatively impacted all resilience indicators and triggered losses of functional response diversity and connectivity across the landscape with undesirable consequences on the capacity of these forests to adapt to global change. Also, current management strategies failed to promote resilience at different spatial levels, highlighting the need for a more active and thoughtful approach to forest management under global change. Our study demonstrates the usefulness of combining dynamic landscapescale simulation modeling with network analyses to evaluate the possible impacts of climate change as well as human and natural disturbances on forest resilience under global change.

Researchers and natural resource managers need predictions of how multiple global changes (e.g., climate change, rising levels of air pollutants, exotic invasions) will affect landscape composition and ecosystem function. Ecological predictive models used for this purpose are constructed using either a mechanistic (process-based) or a phenomenological (empirical) approach, or combination. Given the accelerating pace of global changes, it is becoming increasingly difficult to trust future projections made by phenomenological models estimated under past conditions. Using forest landscape models as an example, I review current modeling approaches and propose principles for developing the next generation of landscape models. First, modelers should increase the use of mechanistic components based on appropriately scaled “first principles” even though such an approach is not without cost and limitations. Second, the interaction of processes within a model should be designed to minimize a priori constraints on process interactions and mimic how interactions play out in real life. Third, when a model is expected to make accurate projections of future system states it must include all of the major ecological processes that structure the system. A completely mechanistic approach to the molecular level is not tractable or desirable at landscape scales. I submit that the best solution is to blend mechanistic and phenomenological approaches in a way that maximizes the use of mechanisms where novel driver conditions are expected while keeping the model tractable. There may be other ways. I challenge landscape ecosystem modelers to seek new ways to make their models more robust to the multiple global changes occurring today.

ContextProximity of landcover elements to each other will enable or constrain fire spread. Assessments of potential fire propagation across landscapes typically involve empirical or simulation models that estimate probabilities based on complex interactions among biotic and abiotic controls.ObjectivesWe developed a metric of landscape fire exposure based solely on a grid cell’s proximity to nearby hazardous fuel capable of transmitting fire to its location. To evaluate accuracy of this new metric, we asked: Do burned areas occur preferentially in locations with high exposure?MethodsWe mapped exposure to hazardous fuels in Alberta, Canada using a neighbourhood analysis. Correspondence between exposure and 2331 fires that burned 2,606,387 ha following our 2007 assessment was evaluated and exposure changes between 2007 and 2019 were assessed.ResultsIn all eleven ecological units analysed, burned area surpluses occurred where exposure was ≥ 60% and corresponding deficits occurred where exposure was < 40%. In seven ecological units, the majority of burned areas had pre-fire exposure ≥ 80%. Between 2007 and 2019, land area with exposure ≥ 80% increased by almost a third.ConclusionsExposure to hazardous fuels is easily quantified with a single thematic layer and aligns well with subsequent fires in Boreal, Foothills and Rocky Mountain natural regions. The resulting fire exposure metric is a numeric rating of the potential for fire transmission to a location given surrounding fuel composition and configuration, irrespective of weather or other fire controls. Exposure can be compared across geographic regions and time periods; and used in conjunction with other metrics of fire controls to inform the study of landscape fire.

The digital revolution is sweeping all areas of human life at an unprecedented speed. Landscape design, as an important discipline that affects people’s quality of life, is also undergoing a digital transformation. This transformation has brought about different modes of thinking and expressions. This paper first introduces digital technology and then explores its application in space creation. Through 3D visualization modeling, an outdoor planning scheme is generated. The study compares the differences between this digital method and the traditional method in terms of frame loss rate and user satisfaction. The results show that the frame loss rate of the 3D visualization method is between 0.2% and 0.4%, and the user satisfaction is 30% higher than that of the traditional method.

ContextRestoring wide areas of fire excluded western US landscapes to fuel limited, fire resilient systems where fires self-regulate and burn with low or mixed severity will require expanded use of both prescribed and natural fire, coupled with strategic mechanical fuels management. However, optimal admixtures of fire and fuel management to set landscapes on trajectories to improve fire resilience and conserve carbon are not well understood.ObjectivesTo understand the effect of accelerating restoration and fuel management in response to potential future fire regimes on a large fire excluded mixed-owner forest landscape.MethodsWe simulated 50-year wildfire and active forest management scenarios on a multi-owner landscape in southcentral Oregon, crossed in a factorial design with a range of wildfire and forest management intensities.ResultsWildfire was more efficient at reducing potential high-severity fire, whereas restoration treatments created patches of fire resilient old forest, especially on federally managed land. With some exceptions, both disturbances reduced aboveground carbon over time, although the magnitude varied among the combinations of fire and active management intensities. We observed interactive effects from specific combinations of fire and management in landscape response metrics compared to stand-alone disturbances.ConclusionsFire and active management have similar landscape outcomes for some but not all restoration objectives, and active management will be required under future predicted fire regimes to conserve and create fire resilient old forest. Achieving widespread fire resilient forest structure will be limited by divergent landowner management behaviors on mixed-owner landscapes.