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The vegetation of alpine tundra is undergoing significant changes and topography has played a significant role in mediating such changes. The roles of topography varied at different scales. In this study, we intended to identify topographic controls on tundra vegetation changes within the Changbai Mountains of Northeast China and reveal the scale effects. We delineated the vegetation changes of the last three decades using the normalized difference vegetation index (NDVI) time series. We conducted a trend analysis for each pixel to reveal the spatial change and used binary logistic regression models to analyze the relationship between topographic controls at different scales and vegetation changes. Results showed that about 30% of tundra vegetation experienced a significant (p < 0.05) change in the NDVI, with 21.3% attributable to the encroachment of low-altitude plants resulting in a decrease in the NDVI, and 8.7% attributable to the expansion of tundra endemic plants resulting in an increase in the NDVI. Plant encroachment occurred more severely in low altitude than in high altitude, whereas plant expansion mostly occurred near volcanic ash fields at high altitude. We found that plant encroachment tended to occur in complex terrains and the broad-scale mountain aspect had a greater effect on plant encroachment than the fine-scale local aspect. Our results suggest that it is important to include the mountain aspect in mountain vegetation change studies, as most such studies only use the local aspect.

For trees, fast growth rates and large size seem to be a fitness benefit because of increased competitiveness, attainment of reproductive size earlier, reduction of generation times, and increased short-term survival chances. However, fast growth rates and large size entail reduced investment in defenses, lower wood density and mechanical strength, increased hydraulic resistance as well as problems with down-regulation of growth during periods of stress, all of which may decrease tree longevity. In this study, we investigated the relationship between longevity and growth rates of trees and quantified effects of spatial environmental variation (elevation, slope steepness, aspect, soil depth) on tree longevity. Radial growth rates and longevities were determined from tree-ring samples of 161 dead trees from three conifer species in subalpine forests of the Colorado Rocky Mountains (Abies lasiocarpa, Picea engelmannii) and the Swiss Alps (Picea abies). For all three species, we found an apparent tradeoff between growth rate to the age of 50 years and longevity (i.e. fast early growth is associated with decreased longevity). This association was particularly pronounced for larger P. engelmannii and P. abies, which attained canopy size, however, there were also significant effects for smaller P. engelmannii and P. abies. For the more shade-tolerant A. lasiocarpa, tree size did not have any effect. Among the abiotic variables tested only northerly aspect significantly favored longevity of A. lasiocarpa and P. engelmannii. Trees growing on south-facing aspects probably experience greater water deficits leading to premature tree death, and/or shorter life spans may reflect shorter fire intervals on these more xeric aspects. Empirical evidence from other studies has shown that global warming affects growth rates of trees over large spatial and temporal scales. For moist-cool subalpine forests, we hypothesize that the higher growth rates associated with global warming may in turn result in reduced tree longevity and more rapid turnover rates.

In 1949–1951, ecologist Robert H. Whittaker sampled plant community composition at 470 sites in the Siskiyou Mountains (Oregon and California; also known as Klamath or Klamath-Siskiyou Mountains). His primary goal was to develop methods to quantify plant community variation across environmental gradients, following on his seminal work challenging communities as discrete entities. He selected the Siskiyous because of their diverse and endemic-rich flora, which he attributed to geological complexity and an ancient stable climate. He chose sites to span gradients of topography, elevation, geologic substrate, and distance from the coast. He used the frequencies of indicator species in his data to assign sampling locations to positions on the topographic gradient, nested within the elevational and substrate gradients. He originated in this study the concept of diversity partitioning, in which gamma diversity (species richness of a community) equals alpha diversity (species richness in homogeneous sites) times beta diversity (species turnover among sites along gradients). Diversity partitioning subsequently became highly influential and new developments on it continue. Whittaker published his Siskiyou work covering paleohistory, biogeography, floristics, vegetation, gradient analysis, and diversity partitioning in Ecological Monographs in 1960. Discussed in 2 pages of his 60-page monograph, diversity partitioning accounts for >95% of its current >4300 citations. In 2006, we retrieved Whittaker’s Siskiyou data in hard copy from the Cornell University archives and entered them in a database. We used these data for multiple published analyses, including some based on (re)sampling the approximate locations of a subset of his sites. Because of the continued interest in diversity partitioning and in historic data sets, here we present his data, including 359 sampling locations and their descriptors and, for each sample, a list of species with their estimated percent cover (herbs and shrubs) and numbers by diameter at breast height (DBH) category (trees). Site descriptors include the approximate location (road, trail, or stream), elevation, topographic aspect, geologic substrate (serpentine, gabbro, or diorite), and dominant woody vegetation of each location. For 111 sites, including the small number chosen to represent the distance-to-coast gradient, we could not locate his data. There are no copyright restrictions and users of these data should cite this data paper in any publications that result from its use. The authors are available for consultations about and collaborations involving the data.

Increasing wildfire frequency and severity in high‐elevation seasonal snow zones presents a considerable water resource management challenge across the western United States (U.S.). Wildfires can affect snowpack accumulation and melt patterns, altering the quantity and timing of runoff. While prior research has shown that wildfire generally increases snow melt rates and advances snow disappearance dates, uncertainties remain regarding variations across complex terrain and the energy balance between burned and unburned areas. Utilizing paired in situ data sources within the 2020 Cameron Peak burn area on the Front Range of Colorado, U.S., during the 2021–2022 winter, we found no significant difference in peak snow water equivalent (SWE) magnitude between burned and unburned areas. However, the burned south aspect reached peak SWE 22 days earlier than burned north. During the ablation period, burned south melt rates were 71% faster than unburned south melt rates, whereas burned north melt rates were 94% faster than unburned north aspects. Snow disappeared 7–11 days earlier in burned areas than unburned areas. Net energy differences at the burned and unburned weather station sites were seasonally variable, the burned area snowpack lost more net energy during the winter, but gained more net energy during the spring. Increased incoming shortwave radiation at the burned site was 6x more impactful in altering the net shortwave radiation balance than the decline in surface albedo. These findings emphasize the need for post‐wildfire water resource planning that accounts for aspect‐dependent differences in energy and mass balance to accurately predict snowpack storage and runoff timing. Plain Language Summary Wildfires are burning more frequently at high‐elevations, where they modify the snowpack. This complicates efforts to predict when snowmelt runoff will occur and the amount of water that will melt from the snowpack. Wildfire generally causes snow to melt earlier in the year and at a faster rate. However, in complex, mountainous terrain, it is not well understood how the magnitude of these changes may differ between neighboring slopes. During the 2021–22 winter in the Cameron Peak burn area (2020) in Colorado, we found that in a high‐elevation snowpack there was no difference in the amount of water accumulated in the snowpack between areas that were burned by the fire and areas that were not. But in areas that burned, the amount of water in the snowpack reached its largest amount 22 days earlier than the areas that did not burn. The snowpack melted faster on both south and north facing slopes in the burned area than comparable unburned areas, causing the burned areas to be snow free 7–11 days earlier. These results highlight the need to account for complex terrain in water resource planning. Key Points The burned south site reached peak snow water equivalent 22 days earlier than all other sites, which peaked simultaneously Burned site melt rates were similar across aspects but exceeded unburned sites by ∼9 mm d−1, causing snow disappearance ∼9 days earlier Burned site net energy balance was dominated by longwave radiation losses in winter and shortwave radiation gains in spring

This study quantifies the effects of topographic aspect on surface fine fuel moisture content (FFMC) in order to better represent landscape-scale variability in fire risk. Surface FFMC in a eucalypt forest was measured from December to May (180 days) on different aspects using a novel method for in situ monitoring of moisture content (GWClit) and temperature (Tlit) in litter. Daily mean GWClit varied systematically with aspect. North (0.07≤GWClit≤1.30kg kg-1) and south (0.11≤GWClit≤1.83kg kg-1) aspects were driest and wettest respectively, whereas east and west were somewhere in between. On the warmest day (38.9°C), the maximum Tlit on north (43.7°C) and south (29.8°C) aspects differed by 13.9°C. Aspect-driven variation in Tlit and GWClit is exacerbated by vegetation, which increases markedly in density with decreasing solar exposure. GWClit was below fibre saturation point (<0.35kg kg-1) on 49 and 128 days on south and north aspects, respectively, demonstrating that fuels beds are often in different stages of drying and therefore subject to different hydrological processes depending on landscape position. This terrain-related variability in moisture dynamics strongly affects the spatial connectivity of fuels, and may be more important for predicting landscape-scale burn outcomes than sub-daily fluctuations at a point.

Most multicellular terrestrial organisms experience climate at scales of millimetres to metres, yet most species-climate associations are analysed at resolutions of kilometres or more. Because individuals experience heterogeneous microclimates in the landscape, species sometimes survive where the average background climate appears unsuitable, and equally may be eliminated from sites within apparently suitable grid cells where microclimatic extremes are intolerable. Local vegetation structure and topography can be important determinants of fine-resolution microclimate, but a literature search revealed that the vast majority of bioclimate studies do not include fine-scale habitat information, let alone a representation of how habitat affects microclimate. In this paper, we show that habitat type (grassland, heathland, deciduous woodland) is a major modifier of the temperature extremes experienced by organisms. We recorded differences among these habitats of more than 5°C in monthly temperature maxima and minima, and of 10°C in thermal range, on a par with the level of warming expected for extreme future climate change scenarios. Comparable differences were found in relation to variation in local topography (slope and aspect). Hence, we argue that the microclimatic effects of habitat and topography must be included in studies if we are to obtain sufficiently detailed projections of the ecological impacts of climate change to develop detailed adaptation strategies for the conservation of biodiversity.

Aboveground net primary productivity (ANPP) in grasslands is an important integrator of terrestrial ecosystem function, a key driver of global biogeochemical cycles, and a critical source of food for wild and domesticated herbivores. ANPP exhibits high spatial and temporal variability, driven by a suite of factors including precipitation amount and pattern, biotic and abiotic legacies, and topographic heterogeneity. Global climate models forecast an altered hydrological cycle due to climate change, including higher precipitation variability and more extreme events, which may further increase spatiotemporal variability in ANPP. Therefore, it is essential to understand the sensitivity of this central ecosystem function to various precipitation metrics, legacies, and topographic positions to better inform sustainable grassland management. In this study, we analyzed long‐term (36‐yr) ANPP data collected across a topographic sequence in the semiarid shortgrass steppe of North America to examine patterns and drivers of spatiotemporal variability in ANPP. We observed that (1) ANPP varied substantially by topographic position, with greater divergence during years with high production, (2) ANPP variability was higher temporally (16‐fold maximum difference across years) than spatially (4‐fold maximum difference across topographic positions), (3) warm‐season perennial grasses were the dominant plant functional type across all topographic positions and strongly influenced total ANPP dynamics, and (4) ANPP had strong sensitivities to current year precipitation amount and pattern that varied by plant functional type, as well as weaker sensitivities to precipitation and productivity legacies. Overall, the lowest topographic position had the highest sensitivity to precipitation, likely due to higher resource availability via the downhill movement of water and nutrients during years with high precipitation and large rainfall events. These results suggest that temporal and spatial ANPP variability in shortgrass steppe is primarily driven by the combined effects of precipitation amount and pattern during the current year, with the dominant warm‐season perennial grasses governing these responses.

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R2 of 0.73 and Root Mean Square Error (RMSE) of 2.21 g/m2. Of the model’s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management.

Available boron (B) is essential to the normal growth of crops. Previous studies on available B have focused on topsoil; hence, information about available B variation and its relationships with environmental variables (topography, climate, vegetation, soil property and parent material) in subsoil is limited. The current study collected 132, 124, and 87 soil samples, respectively, from A, B, and C horizons of arable land in purple hilly areas of southwestern China. Classical statistics, semivariogram analysis, and boosted regression trees (BRT) were applied to investigate available B variation and its affecting factors in various horizons. Samples of each soil horizon were randomly divided into calibration (80%) and validation (20%) sets. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ) were employed for evaluating model performance. Results indicated that available B content decreased with soil depth and showed the strongest spatial autocorrelation in the A horizon. Approximately 50%, 58%, and 51% of available B variability in the three horizons could be explained by the BRT models. Values of MAE varied between 0.038 (C horizon) and 0.053 mg kg −1 (A horizon), and RMSE changed between 0.048 (C horizon) and 0.069 mg kg −1 (A horizon). The relative importance of environmental variables to available B variability varied with soil horizons. Precipitation, flow path length, and topographical aspect were the most critical factors for the A, B, and C horizons, respectively. The importance of valley depth, elevation, and temperature enhanced, whereas precipitation and normalized difference vegetation index decreased in subsoil.

Landslides are one of the most destructive phenomena in nature and damage both property and lives every year. In this paper, a logistic regression model with datasets developed via a geographic information system and remotely sensed data was used to create a landslide spatial susceptibility map for the Three Gorges Project reservoir region on the Yangtze River in Zigui County. The five causative factors used in the logistic regression model were evaluated in different ways: topographic slope and topographic aspect were derived from a topographical map at 1:50,000 scale; bed rock-slope relationship and lithology were obtained from a geological map at 1:50,000 scale; and fractional vegetation cover (FVC), which represents the reduced frequency of landslides due to the vegetation canopy and ground cover and is also one of the most difficult parameters to estimate over broad geographic areas, was generated using a back propagation neural network (BPNN) method based on CBERS (China–Brazil Earth Resources Satellite) data, the results of which were compared with values measured in the field. The obtained Pearson correlation coefficient (r) was 0.899. Then, the FVC factor and the other four factors were used as the input to a logistic regression model. By integrating the five factor maps in the geographical information system (GIS) via pixel-based computing, the landslide spatial susceptibility map was obtained. The study area was reclassified into four categories of landslide susceptibility: severe, moderate, low, and very low. Approximately 15.0 % of the study area was identified as severe susceptibility, and very low, low, and moderate susceptibility zones covered 21.8, 41.7, and 21.5 % of the area, respectively. These results have an accuracy of 78.90 %. Thus, by using a logistic regression model in a GIS environment, a spatial susceptibility map of landslides can be obtained, and the regions in Zigui County that are susceptible to landslides and need immediate protective and mitigation measures can be identified.