Explore the vast range of titles available.

People are constantly changing the land surface through construction, agriculture, energy production, and other activities. Changes both in how land is used by people (land use) and in the vegetation, rock, buildings, and other physical material that cover the Earth's surface (land cover) can be described and future land change can be projected using land-change models (LCMs). LCMs are a key means for understanding how humans are reshaping the Earth's surface in the past and present, for forecasting future landscape conditions, and for developing policies to manage our use of resources and the environment at scales ranging from an individual parcel of land in a city to vast expanses of forests around the world. Advancing Land Change Modeling: Opportunities and Research Requirements describes various LCM approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models. This report provides a summary and evaluation of several modeling approaches, and their theoretical and empirical underpinnings, relative to complex land-change dynamics and processes, and identifies several opportunities for further advancing the science, data, and cyberinfrastructure involved in the LCM enterprise. Because of the numerous models available, the report focuses on describing the categories of approaches used along with selected examples, rather than providing a review of specific models. Additionally, because all modeling approaches have relative strengths and weaknesses, the report compares these relative to different purposes. Advancing Land Change Modeling's recommendations for assessment of future data and research needs will enable model outputs to better assist the science, policy, and decisionsupport communities.

Altered fire regimes can drive major and enduring compositional shifts or losses of forest ecosystems. In western North America, ponderosa pine and dry mixed‐conifer forest types appear increasingly vulnerable to uncharacteristically extensive, high‐severity wildfire. However, unburned or only lightly impacted forest stands that persist within burn mosaics—termed fire refugia—may serve as tree seed sources and promote landscape recovery. We sampled tree regeneration along gradients of fire refugia proximity and density at 686 sites within the perimeters of 12 large wildfires that occurred between 2000 and 2005 in the interior western United States. We used generalized linear mixed‐effects models to elucidate statistical relationships between tree regeneration and refugia pattern, including a new metric that incorporates patch proximity and proportional abundance. These relationships were then used to develop a spatially explicit landscape simulation model. We found that regeneration by ponderosa pine and obligate‐seeding mixed‐conifer tree species assemblages was strongly and positively predicted by refugia proximity and density. Simulation models revealed that for any given proportion of the landscape occupied by refugia, small patches produced greater landscape recovery than large patches. These results highlight the disproportionate importance of small, isolated islands of surviving trees, which may not be detectable with coarse‐scale satellite imagery. Findings also illustrate the interplay between patch‐scale resistance and landscape‐scale resilience: Disturbance‐resistant settings (fire refugia) can entrain resilience (forest regeneration) across the burn matrix. Implications and applications for land managers and conservation practitioners include strategies for the promotion and maintenance of fire refugia as components of resilient forest landscapes.

Climate‐induced vegetation change may be delayed in the absence of disturbance catalysts. However, increases in wildfire activity may accelerate these transitions in many areas, including the western boreal region of Canada. To better understand factors influencing decadal‐scale changes in upland boreal forest vegetation, we developed a hybrid modeling approach that constrains projections of climate‐driven vegetation change based on topo‐edaphic conditions coupled with weather‐ and fuel‐based simulations of future wildfires using Burn‐P3, a spatial fire simulation model. We evaluated eighteen scenarios based on all possible combinations of three fuel assumptions (static, fire‐mediated, and climate‐driven), two fire‐regime assumptions (constrained and unconstrained), and three global climate models. We simulated scenarios of fire‐mediated change in forest composition over the next century, concluding that, even under conservative assumptions about future fire regimes, wildfire activity could hasten the conversion of approximately half of Alberta's upland mixedwood and conifer forest to more climatically suited deciduous woodland and grassland by 2100. When fire‐regime parameter inputs (number of fire ignitions and duration of burning) were modified based on future fire weather projections, the simulated area burned was almost enough to facilitate a complete transition to climate‐predicted vegetation types. However, when fire‐regime parameters were held constant at their current values, the rate of increase in fire probability diminished, suggesting a negative feedback by which a short‐term increase in less‐flammable deciduous forest leads to a long‐term reduction in area burned. Our spatially explicit simulations of fire‐mediated vegetation change provide managers with scenarios that can be used to plan for a range of alternative landscape conditions.

Context Forest insect outbreaks are major landscape-scale disturbances that shape forest composition and dynamics. In eastern North America, the spruce budworm (SBW) is a key defoliator driving these patterns. Understanding how local forest management interacts with outbreak dynamics at broad spatial extents is crucial for designing sustainable management strategies under climate change. Objective We evaluated whether forest management strategies can modify SBW outbreak development across broad spatial extents and over long time frames. Methods We developed a spatially explicit forest landscape model to simulate SBW outbreaks in Quebec, Canada, over 80 years. Simulations were used to compare scenarios with different harvesting rates and regeneration strategies under current and future climate conditions. Results Outbreaks affected from 60,000 to 160,000 km 2 (11–31% of forest area), with climate change driving a gradual northward shift in maximum outbreak impacts. Annual harvesting of 1% of the forest and promoting non-host regeneration (e.g., trembling aspen) reduced SBW-induced forest mortality by up to 30%, particularly when combined with higher harvest intensities that accelerated species turnover and advanced dampening effects by one to two decades. In contrast, prioritizing host species (e.g., black spruce) had little effect on outbreak patterns. Conclusions Local management strategies can substantially influence large-extent outbreak dynamics if applied consistently and intensively. However, our results highlight that even proactive measures will only partially offset future risks, as climate-driven northward shifts may reduce management effectiveness. Spatially explicit models provide valuable insights to design adaptive forest management that mitigates insect disturbances under climate change. Graphical Abstract

Wildlife models focused solely on a single strong influence (e.g., habitat components, wildlife harvest) are limited in their ability to detect key mechanisms influencing population change. Instead, we propose integrated modeling in the context of cumulative effects assessment using multispecies population dynamics models linked to landscape-climate simulation at large spatial and temporal scales.We developed an integrated landscape and population simulation model using ALCES Online as the model-building platform, and the model accounted for key ecological components and relationships among moose (Alces alces), grey wolves (Canis lupus nubilus), and woodland caribou (Rangifer tarandus caribou) in northern Ontario, Canada. We simulated multiple scenarios over 5 decades (beginning 2020) to explore sensitivity to climate change and land use and assessed effects at multiple scales. The magnitude of effect and the relative importance of key factors (climate change, roads, and habitat) differed depending on the scale of assessment. Across the full extent of the study area (654,311 km² [ecozonal scale]), the caribou population declined by 26% largely because of climate change and associated predator-prey response, which led to caribou range recession in the southern part of the study area. At the caribou range scale (108,378 km²), which focused on 2 herds in the northern part of the study area, climate change led to a 10% decline in the population and development led to an additional 7% decline. At the project scale (8,331 km²), which was focused more narrowly on the landscape surrounding 4 proposed mines, the caribou population declined by 29% largely in response to simulated development. Given that observed caribou population dynamics were sensitive to the cumulative effects of climate change, land use, interspecific interactions, and scale, insights from the analysis might not emerge under a less complex model. Our integrated modeling framework provides valuable support for broader regional assessments, including estimation of risk to caribou and Indigenous food security, and for developing and evaluating potential caribou recovery strategies.

ContextIn interior Alaska, increasing wildfire activity associated with climate change is projected to continue, potentially altering regional forest composition. Conifers are emblematic of boreal forest; however, greater frequency and severity of wildfires has been found to favor broadleaf-deciduous species in numerous studies.ObjectivesThis study examines potential shifts in forest type in interior Alaska and how shifts may be impacted by recurring wildfires under future climate change.MethodsA spatially-explicit forest landscape model, LANDIS-II, was used to simulate forest succession and wildfire over a 380,400-hectare landscape under historic and future (RCP 8.5) climate. Wildfire was modeled using the SCRPPLE fire extension and vegetation growth, belowground carbon, hydrologic, and permafrost dynamics were modeled with the DGS succession extension. The relative importance of drivers of forest type change away from black spruce was quantified using random forest models for areas on the landscape experiencing different numbers of wildfires.ResultsGreater frequencies of fire activity were associated with shifts in conifer-dominant areas to broadleaf-deciduous, which climate change accelerated. Vegetation transitions were most strongly influenced by percent tree mortality from the most recent wildfire. Starting deciduous fraction and proximity of mature black spruce to a site pre-fire were also influential, indicating pre-fire composition and context modified the effect of vegetation shifts.ConclusionsThese results underscore how shifts in forest type may occur in a nonlinear manner in this region as the landscape experiences pressure from climate change and forests are subject to complex interactions between wildfire, climate, belowground processes, and the arrangement of forest communities.

Land use and climate change have complex and interacting effects on naturally dynamic forest landscapes. To anticipate and adapt to these changes, it is necessary to understand their individual and aggregate impacts on forest growth and composition. We conducted a simulation experiment to evaluate regional forest change in Massachusetts, USA over the next 50 years (2010-2060). Our objective was to estimate, assuming a linear continuation of recent trends, the relative and interactive influence of continued growth and succession, climate change, forest conversion to developed uses, and timber harvest on live aboveground biomass (AGB) and tree species composition. We examined 20 years of land use records in relation to social and biophysical explanatory variables and used regression trees to create \"probability-of-conversion\" and \"probability-of-harvest\" zones. We incorporated this information into a spatially interactive forest landscape simulator to examine forest dynamics as they were affected by land use and climate change. We conducted simulations in a full-factorial design and found that continued forest growth and succession had the largest effect on AGB, increasing stores from 181.83 Tg to 309.56 Tg over 50 years. The increase varied from 49%% to 112%% depending on the ecoregion within the state. Compared to simulations with no climate or land use, forest conversion reduced gains in AGB by 23.18 Tg (or 18%%) over 50 years. Timber harvests reduced gains in AGB by 5.23 Tg (4%%). Climate change (temperature and precipitation) increased gains in AGB by 17.3 Tg (13.5%%). Pinus strobus and Acer rubrum were ranked first and second, respectively, in terms of total AGB throughout all simulations. Climate change reinforced the dominance of those two species. Timber harvest reduced Quercus rubra from 10.8%% to 9.4%% of total AGB, but otherwise had little effect on composition. Forest conversion was generally indiscriminate in terms of species removal. Under the naïve assumption that future land use patterns will resemble the recent past, we conclude that continued forest growth and recovery will be the dominant mechanism driving forest dynamics over the next 50 years, and that while climate change may enhance growth rates, this will be more than offset by land use, primarily forest conversion to developed uses.

Fire is a critical ecological process to the forests of the Southern Appalachians. Where fire was excluded from forest types that historically burned frequently, unanticipated changes can occur when fire is reintroduced. For example, the development of new fuel characteristics can change the patterns of fire mortality and associated ecological responses. To test the fire effects of delayed fire mortality (mortality initiated by fire that occurs subsequent to the fire year) in the Southern Appalachians, USA, we developed a fire‐effects model using both field studies and remote sensing. We then simulated these effects at a landscape scale to estimate broader ecological effects. Fire‐effects models that accounted for delayed mortality increased landscape biomass removed annually (~23%) and increased the number of sites with high light conditions (leaf area index < 4) when compared to simulations that only account for immediate mortality. While delayed mortality occurred across species and age classes, it was especially prevalent among older trees (>100 years old) and fire‐resistant species (Quercus spp.). Overall, regeneration (trees <20 years old) changed very little, even with the inclusion of delayed mortality. This evidence suggests that, even when accounting for delayed mortality, individual fires are unlikely to shift the landscape composition toward the conditions of forests prior to fire exclusion and may even increase mesophication long term due to the loss of overstory dominant xeric trees.

Since the beginning of the 21st century, the economic development of Tibet has been accelerating. The Yarlung Zangbo River and its two tributaries, as the region with the largest population concentration and the fastest economic development in Tibet, has been under the dual influence of global climate warming and the intensification of human social and economic activities, where a high number of land types, such as woodland, grassland, and water areas, have been transformed into other land types, and the residential area has expanded in a disorderly manner. The ability to maintain sustainable regional development has been severely damaged. To meet the requirements of different stages of social and economic development and regional social development goals, in this study, we use the Yarlung Zangbo River and its two tributaries of Tibet as an example. Based on the Markov–PLUS model and considering the natural, social, and cultural conditions of the basin, combined with the multi-landscape simulation of land use, we predict the land use situation of the Yarlung Zangbo River and its two tributaries of Tibet in 2038. We observed the following: (i) the Markov–PLUS model has a high simulation accuracy for different land types in the study area, and can sufficiently simulate the changes in different land types in the Yarlung Zangbo River and its two tributaries of Tibet; and (ii) the simulation settings of the three landscapes basically meet the different development modes and paths of the basin in the future. There were obvious differences in the structure of land use in the basin, among which there were obvious differences, especially agricultural land and water areas. Use of the Markov–PLUS model can provide data support and references for the implementation in terms of ecological scrutiny, landscape planning, and early warnings for food production consumption security and unreasonable land use, in order to achieve the sustainable development of the basin.