Modeling Spatial Patterns of Humus Forms in Montane and Subalpine Forests: Implications of Local Variability for Upscaling

Humus forms are a distinctive morphological indicator of soil organic matter decomposition. The spatial distribution of humus forms depends on environmental factors such as topography, climate and vegetation. In montane and subalpine forests, environmental influences show a high spatial heterogeneity, which is reflected by a high spatial variability of humus forms. This study aims at examining spatial patterns of humus forms and their dependence on the spatial scale in a high mountain forest environment (Val di Sole/Val di Rabbi, Trentino, Italian Alps). On the basis of the distributions of environmental covariates across the study area, we described humus forms at the local scale (six sampling sites), slope scale (60 sampling sites) and landscape scale (30 additional sampling sites). The local variability of humus forms was analyzed with regard to the ground cover type. At the slope and landscape scale, spatial patterns of humus forms were modeled applying random forests and ordinary kriging of the model residuals. The results indicate that the occurrence of the humus form classes Mull, Mullmoder, Moder, Amphi and Eroded Moder generally depends on the topographical position. Local-scale patterns are mostly related to micro-topography (local accumulation and erosion sites) and ground cover, whereas slope-scale patterns are mainly connected with slope exposure and elevation. Patterns at the landscape scale show a rather irregular distribution, as spatial models at this scale do not account for local to slope-scale variations of humus forms. Moreover, models at the slope scale perform distinctly better than at the landscape scale. In conclusion, the results of this study highlight that landscape-scale predictions of humus forms should be accompanied by local- and slope-scale studies in order to enhance the general understanding of humus form patterns.

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Climate impacts on tree growth in a Neotropical high mountain forest of the Peruvian Andes

iForest - Biogeosciences and Forestry ◽

10.3832/ifor3124-013 ◽

2020 ◽

Vol 13 (1) ◽

pp. 194-201

Author(s):

C Rodríguez-Morata ◽

J Madrigal-González ◽

M Stoffel ◽

JA Ballesteros-Cánovas

Keyword(s):

Tree Growth ◽

Climate Impacts ◽

Mountain Forest ◽

High Mountain ◽

Peruvian Andes

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Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

Earth System Dynamics ◽

10.5194/esd-6-61-2015 ◽

2015 ◽

Vol 6 (1) ◽

pp. 61-81 ◽

Cited By ~ 28

Author(s):

L. Gerlitz ◽

O. Conrad ◽

J. Böhner

Keyword(s):

Neural Network ◽

High Resolution ◽

Spatial Resolution ◽

Large Scale ◽

Local Scale ◽

Predictor Variables ◽

High Mountain ◽

Target Area ◽

Monthly Precipitation ◽

Highly Nonlinear

Abstract. The heterogeneity of precipitation rates in high-mountain regions is not sufficiently captured by state-of-the-art climate reanalysis products due to their limited spatial resolution. Thus there exists a large gap between the available data sets and the demands of climate impact studies. The presented approach aims to generate spatially high resolution precipitation fields for a target area in central Asia, covering the Tibetan Plateau and the adjacent mountain ranges and lowlands. Based on the assumption that observed local-scale precipitation amounts are triggered by varying large-scale atmospheric situations and modified by local-scale topographic characteristics, the statistical downscaling approach estimates local-scale precipitation rates as a function of large-scale atmospheric conditions, derived from the ERA-Interim reanalysis and high-resolution terrain parameters. Since the relationships of the predictor variables with local-scale observations are rather unknown and highly nonlinear, an artificial neural network (ANN) was utilized for the development of adequate transfer functions. Different ANN architectures were evaluated with regard to their predictive performance. The final downscaling model was used for the cellwise estimation of monthly precipitation sums, the number of rainy days and the maximum daily precipitation amount with a spatial resolution of 1 km2. The model was found to sufficiently capture the temporal and spatial variations in precipitation rates in the highly structured target area and allows for a detailed analysis of the precipitation distribution. A concluding sensitivity analysis of the ANN model reveals the effect of the atmospheric and topographic predictor variables on the precipitation estimations in the climatically diverse subregions.

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Evaluating Predictor Strategies for Regression-Based Downscaling with a Focus on Glacierized Mountain Environments

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0215.1 ◽

2017 ◽

Vol 56 (6) ◽

pp. 1707-1729 ◽

Cited By ~ 4

Author(s):

Marlis Hofer ◽

Johanna Nemec ◽

Nicolas J. Cullen ◽

Markus Weber

Keyword(s):

Large Scale ◽

Global Radiation ◽

Local Scale ◽

Forecast Skill ◽

Model Complexity ◽

High Mountain ◽

European Alps ◽

Limited Information ◽

Mountain Environments ◽

Measuring Site

AbstractThis study explores the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity, and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in close proximity to mountain glaciers: 1) the Vernagtbach station in the European Alps, 2) the Artesonraju measuring site in the tropical South American Andes, and 3) the Mount Brewster measuring site in the Southern Alps of New Zealand. The large-scale dataset being evaluated is the ERA-Interim dataset. In the downscaling procedure, particular emphasis is put on developing efficient yet not overfit models from the limited information in the temporally short (typically a few years) observational records of the high mountain sites. For direct (univariate) predictors, optimum scale analysis turns out to be a powerful means to improve the forecast skill without the need to increase the downscaling model complexity. Yet the traditional (multivariate) predictor sets show generally higher skill than the direct predictors for all variables, sites, and days of the year. Only in the case of large sampling uncertainty (identified here to particularly affect observed precipitation) is the use of univariate predictor options justified. Overall, the authors find a range in forecast skill among the different predictor options applied in the literature up to 0.5 (where 0 indicates no skill, and 1 represents perfect skill). This highlights that a sophisticated predictor selection (as presented in this study) is essential in the development of realistic, local-scale scenarios by means of downscaling.

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Forest fragmentation and forest management: A plea for empirical data

The Forestry Chronicle ◽

10.5558/tfc78686-5 ◽

2002 ◽

Vol 78 (5) ◽

pp. 686-689 ◽

Cited By ~ 13

Author(s):

Robert G G. D'Eon

Keyword(s):

Forest Management ◽

Forest Fragmentation ◽

Habitat Loss ◽

Spatial Patterns ◽

Empirical Data ◽

Landscape Scale ◽

Managed Forests ◽

Habitat Loss And Fragmentation ◽

Theoretical Predictions ◽

Forest Managers

Forest fragmentation is one of the most important conservation issues of recent times. Most of what we know about forest fragmentation is based on speculation and untested theory due to a paucity of empirical data. The lack of empirical data can be attributed to (1) the extreme difficulty in conducting good fragmentation studies, and (2) confusion between habitat loss and fragmentation effects. Empirical data from well-designed fragmentation studies is direly needed to validate theoretical predictions stemming from the fragmentation paradigm. Since the best data will come from landscape-scale experiments in managed forests, partnerships and support from researchers and forest managers is critical in this pursuit. Key words: empirical data, forest fragmentation, landscape spatial patterns

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Local-scale spatial patterns of freshwater mussels in the Upper Mississippi River

Freshwater Science ◽

10.1086/705917 ◽

2019 ◽

Vol 38 (4) ◽

pp. 742-752 ◽

Cited By ~ 1

Author(s):

P. R. Ries ◽

N. De Jager ◽

T. J. Newton ◽

S. J. Zigler

Keyword(s):

Spatial Patterns ◽

Mississippi River ◽

Freshwater Mussels ◽

Local Scale ◽

Upper Mississippi River

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Interactions between large herbivores and litter removal by termites across a rainfall gradient in a South African savanna

Journal of Tropical Ecology ◽

10.1017/s0266467411000125 ◽

2011 ◽

Vol 27 (4) ◽

pp. 375-382 ◽

Cited By ~ 9

Author(s):

Robert Buitenwerf ◽

Nicola Stevens ◽

Cleo M. Gosling ◽

T. Michael Anderson ◽

Han Olff

Keyword(s):

Plant Biomass ◽

Spatial Scales ◽

Landscape Scale ◽

Local Scale ◽

Arid Ecosystems ◽

Annual Rainfall ◽

Large Herbivores ◽

Mammalian Herbivores ◽

Litter Removal ◽

Semi Arid

Abstract:Litter-feeding termites influence key aspects of the structure and functioning of semi-arid ecosystems around the world by altering nutrient and material fluxes, affecting primary production, foodweb dynamics and modifying vegetation composition. Understanding these complex effects depends on quantifying spatial heterogeneity in termite foraging activities, yet such information is scarce for semi-arid savannas. Here, the amount of litter that was removed from 800 litterbags in eight plots (100 litterbags per plot) was measured in Hluhluwe–iMfolozi Park (HiP) South Africa. These data were used to quantify variation in litter removal at two spatial scales: the local scale (within 450-m2 plots) and the landscape scale (among sites separated by 8–25 km). Subsequently, we attempted to understand the possible determinants of termites’ foraging patterns by testing various ecological correlates, such as plant biomass and bare ground at small scales and rainfall and fences that excluded large mammalian herbivores at larger scales. No strong predictors for heterogeneity in termite foraging intensity were found at the local scale. At the landscape scale termite consumption depended on an interaction between rainfall and the presence of large mammalian herbivores: litter removal by termites was greater in the presence of large herbivores at the drier sites but lower in the presence of large herbivores at the wetter sites. The effect of herbivores on termite foraging intensity may indicate a switch between termites and large herbivore facilitation and competition across a productivity gradient. In general, litter removal decreased with increasing mean annual rainfall, which is in contrast to current understanding of termite consumption across rainfall and productivity gradients. These results generate novel insights into termite ecology and interactions among consumers of vastly different body sizes across spatial scales.

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DECAY IN SUBALPINE SPRUCE ON THE ROCKY MOUNTAIN FOREST RESERVE IN ALBERTA

Canadian Journal of Botany ◽

10.1139/b56-062 ◽

1956 ◽

Vol 34 (5) ◽

pp. 805-816 ◽

Cited By ~ 8

Author(s):

D. E. Etheridge

Keyword(s):

Picea Glauca ◽

Rocky Mountain ◽

Ground Cover ◽

Major Portion ◽

Mountain Forest ◽

Forest Reserve ◽

Butt Rot ◽

Coniophora Puteana ◽

The Third ◽

Unidentified Fungus

Data from 456 living subalpine spruce on six 0.1-ac. plots in the Bow River Forest in 1950 and six 0.2-ac. plots in the Crowsnest, Bow River, and Clearwater Forests in 1952 show a total of 203 separate infections of which approximately half occurred in the basal part of the trees. Most of the butt rot was associated with Polyporus circinatus var. dualis Peck, Flammula connissans Fr., and an unidentified fungus designated "Unknown C". Coniophora puteana (Schum. ex Fr.) Karst. was the fungus associated with the major portion of the brown butt rot. Among the white trunk rots, Stereum sanguinolentum Alb. & Schw. ex Fr. was the fungus most frequently isolated from infected trees and Fames pint (Thore) Lloyd was responsible for the largest, cull losses. Peniophora septentrionalis Laurila, which was isolated from Picea glauca (Moench) Voss and P. engelmannii Parry, was the third most important fungus associated with trunk rot. Trunk rots account for 70% of the decay losses while fungi producing white rots account for 93.6% of the total decay. The incidence of decay increased progressively with age at different rates for trees on "dry" and "moist" sites. The two sites are characterized by distinctive ground cover associations.

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Exogenous disturbances and endogenous self-organized processes are not mutually exclusive drivers of spatial patterns in macroalgal assemblages

10.1101/2020.08.25.265967 ◽

2020 ◽

Author(s):

J. He ◽

L. Rindi ◽

C. Mintrone ◽

L. Benedetti-Cecchi

Keyword(s):

Spatial Patterns ◽

Empirical Work ◽

Power Laws ◽

Local Scale ◽

Species Assemblages ◽

Spatial Gradient ◽

Gap Size ◽

Scale Interactions ◽

Relative Contribution ◽

Self Organized

AbstractComplex spatial patterns are common in coastal marine systems, but mechanisms underlying their formation are disputed. Most empirical work has focused on exogeneous spatially structured disturbances as the leading cause of pattern formation in species assemblages. However, theoretical and observational studies suggest that complex spatial patterns, such as power laws in gap-size distribution, may result from endogenous self-organized processes involving local-scale interactions. The lack of studies simultaneously assessing the influence of spatially variable disturbances and local-scale interactions has fuelled the idea that exogenous and endogenous processes are mutually exclusive explanations of spatial patterns in marine ecosystems. To assess the relative contribution of endogenous and exogenous processes in the emergence of spatial patterns, an intertidal assemblage of algae and invertebrates was exposed for 2 years to various combinations of intensity and spatial patterns of disturbance. Localized disturbances impinging at the margins of previously disturbed clearings and homogenous disturbances without any spatial pattern generated heterogeneous distributions of disturbed gaps and macroalgal patches, characterized by a truncated or a pure power-law scaling. Spatially varying disturbances produced a spatial gradient in the distribution of algal patches and, to a lesser extent, also a power-scaling in both patch- and gap-size distributions. These results suggest that exogenous and endogenous processes are not mutually exclusive forces that can lead to the formation of similar spatial patterns in species assemblages.

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