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Climate region (a) and forest type maps (b) of the study area. Five climate regions were determined by a combination of summer (June, July, August) mean maximum daily temperature and mean precipitation. Ponderosa pine/dry mixed‐conifer forest is dominated by ponderosa pine, moist mixed‐conifer forest is dominated by grand fir, and high‐elevation forest is co‐dominated by Engelmann spruce and sub‐alpine fir.
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Climate warming in the western United States is causing changes to the wildfire regime in mixed‐conifer forests. Rising temperatures, longer fire seasons, increased drought, as well as fire suppression and changes in land use, have led to greater and more severe wildfire activity, all contributing to altered forest composition over the past century...
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... High-severity fire combined with warmer and drier conditions is already decreasing conifer regeneration in the western United States (Stevens-Rumann et al., 2018) and projections under future climate show limited conifer regeneration following high-severity wildfire in southwestern US forests (Jung et al., 2023;Keyser et al., 2020). A greater increase in high-severity fires occurring at high elevation in mixed-conifer forests compared with lower elevations is also expected, leading to high mortality of the dominant species, Engelmann spruce and subalpine fire, which are not adapted to fire (Cassell et al., 2019;Flatley & Fulé, 2016;Remy et al., 2021). ...
... Changing climate is affecting tree growth and mortality rates and, when combined with wildfire, can cause rapid shifts in vegetation type (Remy et al., 2021;Stevens-Rumann et al., 2018). While management treatments, including combinations of thinning and burning, can alter resource competition and may increase forest resilience to changing climate, it is not clear if the aridification of the southwest will allow for the same amount of treatment efficacy as has been found in other forest types (Cassell et al., 2019;Liang et al., 2018). Similar to other studies, treatments decreased the probability of high-severity fires in areas initially at high risk of high-severity fire under future climate conditions, but the structural and compositional response of the forests differed along the elevation gradient (Krofcheck et al., 2017;Liang et al., 2018;Loehman et al., 2018;McCauley et al., 2019). ...
In southwestern US forests, the combined impact of climate change and increased fuel loads due to more than a century of human‐caused fire exclusion is leading to larger and more severe wildfires. Restoring frequent fire to dry conifer forests can mitigate high‐severity fire risk, but the effects of these treatments on the vegetation composition and structure under projected climate change remain uncertain.
We used a forest landscape model to assess the impact of thinning and prescribed burns in dry conifer forests across an elevation gradient, encompassing low‐elevation pinyon‐juniper woodlands, mid‐elevation ponderosa pine and high‐elevation mixed‐conifer forests.
Our results demonstrated that the treatments decreased the probability of high‐severity fires by 42% in the study area. At low elevation, the treatments did not prevent loss in forest cover and biomass with decreases in Pinus edulis and Juniperus monosperma abundances. At mid‐elevation, changes in fire effects maintained a greater diversity of tree species by favouring the maintenance of cohorts of old trees, in particular Pinus ponderosa which accumulated 5.41 Mg ha⁻¹ more above‐ground biomass than without treatments by late‐century. Treatments in dry conifer forests modified fire effects beyond the treated area, resulting in increased cover and biomass of old Picea englemannii and Abies lasiocarpa cohorts.
Synthesis and applications: Our findings indicate that thinning and prescribed burning can enhance tree species diversity in dry conifer forests by protecting old cohorts from stand‐replacing fires. Moreover, our results suggest that treatments mainly implemented in dry pine forests with high risk of high‐severity fires can be beneficial for subalpine species conservation by reducing the chance that high‐severity fire at mid‐elevation is transmitted into high‐elevation forest.
... Using the raster-based dynamic forest landscape model LANDIS-II (Scheller et al., 2007), we simulated changes in C storage and fire regimes within our study landscape in the Siskiyou Mountains. This model has been widely used to simulate forest succession and response to disturbance, treatment, and climate change scenarios in the western United States (Cassell et al., 2019;Duveneck et al., 2014;Liang et al., 2018;Serra-Diaz et al., 2018;Syphard et al., 2011). We chose the LANDIS-II modeling framework due to its unique ability to simulate the effects of climate change, prescribed fire, and wildfire on forest succession and C at a landscape scale over long time periods. ...
Prescribed fire has been increasingly promoted to reduce wildfire risk and restore fire‐adapted ecosystems. Yet, the complexities of forest ecosystem dynamics in response to disturbances, climate change, and drought stress, combined with myriad social and policy barriers, have inhibited widespread implementation. Using the forest succession model LANDIS‐II, we investigated the likely impacts of increasing prescribed fire frequency and extent on wildfire severity and forest carbon storage at local and landscape scales. Specifically, we ask how much prescribed fire is required to maintain carbon storage and reduce the severity and extent of wildfires under divergent climate change scenarios? We simulated four prescribed fire scenarios (no prescribed fire, business‐as‐usual, moderate increase, and large increase) in the Siskiyou Mountains of northwest California and southwest Oregon. At the local site scale, prescribed fires lowered the severity of projected wildfires and maintained approximately the same level of ecosystem carbon storage when reapplied at a ~15‐year return interval for 50‐year simulations. Increased frequency and extent of prescribed fire decreased the likelihood of aboveground carbon combustion during wildfire events. However, at the landscape scale, prescribed fire did not decrease the projected severity and extent of wildfire, even when large increases (up to 10× the current levels) of prescribed fire were simulated. Prescribed fire was most effective at reducing wildfire severity under a climate change scenario with increased temperature and precipitation and on sites with north‐facing aspects and slopes greater than 30°. Our findings suggest that placement matters more than frequency and extent to estimate the effects of prescribed fire, and that prescribed fire alone would not be sufficient to reduce the risk of wildfire and promote carbon sequestration at regional scales in the Siskiyou Mountains. To improve feasibility, we propose targeting areas of high concern or value to decrease the risk of high‐severity fire and contribute to meeting climate mitigation and adaptation goals. Our results support strategic and targeted landscape prioritization of fire treatments to reduce wildfire severity and increase the pace and scale of forest restoration in areas of social and ecological importance, highlighting the challenges of using prescribed fire to lower wildfire risk.
... Prolonged drought can increase temperatures. High temperatures will cause leaves, twigs, and wood to dry out, triggering fires [6,7]. In addition, drought can also cause water shortages [8]. ...
Changes in global weather patterns are sweeping the world, including Indonesia. One of the causes of this change was the El Niño event, where sea surface temperatures in the central Pacific Ocean experienced an increase. Apart from causing temperatures to increase, it also causes the intensity of rainfall to decrease, causing drought disasters. Anticipating natural disasters and disaster mitigation needs to be carried out to reduce their negative impacts. Efforts can be made by identifying areas with a high potential for drought and clustering areas based on the level of potential drought. This article focuses on extreme data from maximum temperatures in 34 provinces in Indonesia. Clustering was performed using the k-means and k-medoids methods and evaluated using the Davies-Bouldin index. Predict the highest maximum temperature in a specific period using the return level. The result shows that the k-means method is more suitable and better implemented by checking on the Davies-Boulding index, which is 0.9945.
... The CV's plant community exhibited a high species richness, with 108 species distributed across 39 distinct botanical families ( Table 1). The prominent representatives were Fabaceae (14), Myrtaceae (11), Euphorbiaceae (8), Rutaceae (7), Lauraceae (6), Moraceae (60), Arecaceae (5), and Meliaceae (5). The remaining families each had three or fewer species. ...
The invasive Hovenia dulcis is considered the main invasive species in the Atlantic Forest, capable of altering environmental conditions on a local scale and producing profound changes in the composition of the plant community. Combining images obtained by drones and satellite imagery can make forest monitoring more efficient. It enables a more efficient and targeted response to contain the spread of invasive species. Additionally, phytosociological studies can contribute to understanding the structure of the native tree community and the impacts generated by biological invasion. This research aimed to use CBERS4-A satellite images with a high-resolution suitable for applying land use and land cover classification methods. Automatic supervised object-directed classification was performed through the Dzetsaka Classification Tool, using the Gaussian Mixture Model method. 150 hectares of georeferenced orthomosaics obtained through drones was using to confirm the identification of the invader. The entire area was traversed to determine the tree community, and 72 random sample plots were established, each with a fixed area of 100 m². Calculated indices of Shannon Index (H’) = 3.65 and Uniformity (J’) = 78% demonstrate that the plant community has a high diversity of species. H. dulcis had the highest number of sampled individuals (146), being the species with the highest Relative Density (9.14) within the community, and the second-highest in Relative Frequency (5.10%), Coverage Importance Value (8.85%), and Importance Value Index (7.60%). The methodology employed for invader identification through satellite and drone images allowed for rapid and precise data collection, covering an area of 86.44 hectares.
... For example, the 2011 Las Conchas Fire destroyed approximately 156,000 ha of predominately forest habitat in the Jemez Mountains (in and around BAND), while the 2006 Warm Fire burned 15,800 ha on GRCA's Kaibab Plateau. Such fires may destroy large expanses of mixed-conifer forest, fragment remaining habitat into patches, homogenize tree species composition by removing subalpine species (Cassell et al. 2019), and result in delayed tree mortality in the decades following the fire (Stoddard et al. 2018). While wildfire is a natural component of Southwestern conifer forest ecology, recent megafires, often in association with climatedriven changes to aridity, are causing regeneration failures in burn patches that have resulted in type conversion to nonforest habitat (Stevens-Rumann et al. 2022), or changes in dominance to quaking aspen (Populus tremuloides) stands (Kulakowski et al. 2013). ...
Climate change is considered a major driver of recent avian population declines, particularly in the drought-stricken southwestern United States. Predicting how bird populations will respond requires understanding the climatic drivers influencing population density across the region’s diverse habitats. We modelled breeding-season densities of 50 bird species in relation to spring and summer drought and the timing of North American monsoon rainfall over a 12-year period (2007–2018) and across 4 habitats comprising an approximately 1,500 m elevational gradient. We estimated annual breeding-season population density in relation to climate in the previous year by fitting a Bayesian hierarchical N-mixture model to point-count data from each of 6 national parks on the Colorado Plateau. Specifically, we asked whether (1) population trends were stable, increasing, or decreasing in the focal parks; (2) breeding densities were affected by drought or the timing of monsoon rains; and (3) climatic effects differed across habitat types and among species that molt on the breeding grounds, the nonbreeding grounds, or stopover to molt in the monsoon region of northwestern Mexico (molt migrants). Population trends varied with habitat. Species of high-elevation mixed-conifer forest declined over the study period, matching regional Breeding Bird Survey trends, likely in response to climate-related habitat loss and disturbance. By contrast, lower-elevation pinyon-juniper and grassland-shrubland species density generally increased. Effects of drought varied by habitat with elevation: mixed-conifer species responded positively to drought in the previous year, likely due to earlier snowmelt and breeding phenology, whereas pinyon-juniper species were unaffected, and grassland-shrubland species responded negatively, perhaps due to reduced nest survival. Later arrival of monsoon rains, a common prediction of climate models, had a positive effect on grassland bird densities, but a negative effect on molt-migrant densities. Late monsoon rains may result in a phenological mismatch between migration timing and the pulse of resources required to molt.
... For example, in mixed conifer forests, Ponisio et al. (2016) found that flowering plant richness increased with pyrodiversity, except after high severity fires, when richness declined with pyrodiversity. Large fires also can create large, homogenous areas of vegetation of the same seral stage (Cassell et al., 2019), which may reduce species richness and diversity between sites (i.e., beta diversity) within landscapes as entire landscapes are reduced to the same time-since-fire age (Farnsworth et al., 2014). Previous work indicates that Indigenous pyrodiversity can reduce the spread of wildfires (Greenwood et al., 2021), even under fluctuating rainfall (Bliege Bird et al., 2012). ...
... The ability to collect tree-level information also has important implications for evaluating wildfire risk in the context of fuel management. Information such as fuel density, the distribution of crown height and species of trees are important for inferring characteristics of wildfires [18,6]. The proposed method in this paper can be utilized to measure current density and predict growth trends. ...
Accurate forest inventory data are essential for tracking carbon sequestration, estimating carbon emissions from deforestation, assessing plant and animal habitats for biodiversity, and predicting environmental risks such as wildfires. Traditional methods of data collection have faced challenges in either scale or precision. The advent of terrestrial laser scanners addressed some of these issues but faced limitations in cost and mobility. This paper proposes a new approach using mobile LIDAR for forest inventory data collection. By integrating advancements in computer vision, the methodology aims to provide comprehensive individual tree data, including parameters like diameter at breast height, volume estimations, species identification, and temporal tracking of individual trees. This proposed research direction addresses current gaps in the use of LIDAR and camera inference for forestry data where existing work does not generate domain context-aware data by narrowly focusing collection on isolated tree attributes.
... In light of the data we present here, very large burn footprints would likely be mathematically necessary for landscapes to have attained such large area burned historically. Thus, while some large fires can be clearly detrimental or create conditions outside historical/characteristic behavior for a given landscape (e.g., very large stand-replacement patches in dry forests; Cansler and McKenzie, 2014;Coop et al., 2020;Cassell et al., 2019;Singleton et al., 2021;Churchill et al., 2022, Povak et al., 2020Cova et al., 2023), simply being large is not itself sufficient to count a fire as problematic. ...
... The increase of severe droughts due to climate change has put increased stress on forest ecosystems (Cassell, Scheller, Lucash, Hurteau, & Loudermilk, 2019). Tree regeneration, a key indicator of forest resilience, has decreased over the years due to effects from climate change (Stevens-Rumann et al., 2018). ...
Forested landscapes in the Western United States are subject to growing size and severity of wildfires, in part due to historical management strategies focusing on wildfire suppression. Forest restoration treatments and fuels reductions, including thinning and prescribed burning, can reduce the frequency and intensity of wildfires. Extensive restoration and fuels treatment efforts are underway across many areas in the Southwestern United States, including New Mexico. The tradeoff between amenity values provided by forested landscapes and the wildfire risk associated with forested landscapes is becoming increasingly important to understand as development in the wildland-urban interface increases. Understanding how house proximity, relative to forest restoration or fuels treatments, is capitalized into home sale prices can provide useful information about how individuals value forested landscapes that have been altered to reduce wildfire risk or severity. We use a Hedonic Property Model to estimate the average treatment effect of proximity to forest restoration or fuel treatments in New Mexico, United States. We use matching methods to estimate the average treatment effect of proximity to forest restoration. We find that proximity to the forest has a positive amenity value; however, proximity to recent forest restoration or fuel treatments results in a decrease in house sale prices. We combine the results of our two models and calculate that homes not within one kilometer of a treated forest and within one kilometer of Cibola National Forest sell for an average $73,626 premium. The average premium drops to $22,996 for homes within one kilometer of a forest that has been recently treated and within one kilometer of Cibola National Forest.
... Global forest health is inherently linked to climate change. Forests face risk from increasing fires (in frequency and intensity) [1,2] and drought [3,4]. Forests themselves influence the trajectory of climate change with deforestation and forest disturbance contributing 6%-17% of anthropogenic greenhouse gas emissions [5,6], while carbon sequestered by reforestation and forest growth can conversely reduce atmospheric carbon concentrations [7]. ...
Local decisionmakers managing forests for sequestering carbon and mitigating disturbance face increasing pressures from climate change, while needing to derive revenue from forests resources and balance diverse stakeholder concerns. Decision support systems (DSS) that use freely available satellite data are effective tools for forest management decision-making, due to the broad spatial coverage and temporal consistency of this data. However, the literature demonstrates that many DSS developed by researchers incorporating satellite data are often not utilized by decision makers. This is in part due to a lack of stakeholder engagement in DSS development. Here, we employ an intentional stakeholder engagement process to develop an accessible DSS incorporating satellite data. This process is applied with the Yurok Tribe – an Indigenous community in California – towards the goal of managing forests for carbon sequestration. We use system architecture framework (SAF) to translate stakeholder objectives into targeted analyses and the environment-vulnerability-decision-technology (EVDT) integrated modeling framework to incorporate contextual local infrastructure and land ownership information alongside satellite data analyses. Satellite data analyses classify tree cover and track Normalized Difference Vegetation Index (NDVI) over time. We compare and show that trends in our analysis agree with independent global tree cover and biomass datasets. These analyses meet the Tribe's objective for tracking forest trends in Yurok carbon sequestration projects, while also detecting undesirable conifer encroachment on culturally-important prairies traditionally managed as open landscapes. Additionally, integration of community infrastructure data alongside satellite-derived drought, forest fire, and smoke density information via EVDT meets another Yurok Tribe objective of identifying negatively impacted Tribal areas for aid allocation. User feedback sessions with participating Yurok Tribe employees assess the prototype DSS and show 80% of users providing high ratings (≥75%) for information accuracy, relevancy, and sufficiency, demonstrating that our DSS development process overcomes previous gaps to DSS use and satellite data accessibility to augment Yurok Tribe forest management decision making.
