About: Oecologia is an academic journal. The journal publishes majorly in the area(s): Population & Predation. It has an ISSN identifier of 0029-8549. Over the lifetime, 13688 publication(s) have been published receiving 910711 citation(s).
Topics: Population, Predation, Species richness, Foraging, Biological dispersal
Papers published on a yearly basis
TL;DR: Transitions are proposed for species data tables which allow ecologists to use ordination methods such as PCA and RDA for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases.
Abstract: This paper examines how to obtain species biplots in unconstrained or constrained ordination without resorting to the Euclidean distance [used in principal-component analysis (PCA) and redundancy analysis (RDA)] or the chi-square distance [preserved in correspondence analysis (CA) and canonical correspondence analysis (CCA)] which are not always appropriate for the analysis of community composition data. To achieve this goal, transformations are proposed for species data tables. They allow ecologists to use ordination methods such as PCA and RDA, which are Euclidean-based, for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases. This allows the use of the original (transformed) species data in RDA carried out to test for relationships with explanatory variables (i.e. environmental variables, or factors of a multifactorial analysis-of-variance model); ecologists can then draw biplots displaying the relationships of the species to the explanatory variables. Another application allows the use of species data in other methods of multivariate data analysis which optimize a least-squares loss function; an example is K-means partitioning.
TL;DR: Surviving in certain environments clearly does not require maximising photosynthetic capacity for a given leaf nitrogen content, as variation reflects different strategies of nitrogen partitioning, the electron transport capacity per unit of chlorophyll and the specific activity of RuBP carboxylase.
Abstract: The photosynthetic capacity of leaves is related to the nitrogen content primarily bacause the proteins of the Calvin cycle and thylakoids represent the majority of leaf nitrogen. To a first approximation, thylakoid nitrogen is proportional to the chlorophyll content (50 mol thylakoid N mol-1 Chl). Within species there are strong linear relationships between nitrogen and both RuBP carboxylase and chlorophyll. With increasing nitrogen per unit leaf area, the proportion of total leaf nitrogen in the thylakoids remains the same while the proportion in soluble protein increases. In many species, growth under lower irradiance greatly increases the partitioning of nitrogen into chlorophyll and the thylakoids, while the electron transport capacity per unit of chlorophyll declines. If growth irradiance influences the relationship between photosynthetic capacity and nitrogen content, predicting nitrogen distribution between leaves in a canopy becomes more complicated. When both photosynthetic capacity and leaf nitrogen content are expressed on the basis of leaf area, considerable variation in the photosynthetic capacity for a given leaf nitrogen content is found between species. The variation reflects different strategies of nitrogen partitioning, the electron transport capacity per unit of chlorophyll and the specific activity of RuBP carboxylase. Survival in certain environments clearly does not require maximising photosynthetic capacity for a given leaf nitrogen content. Species that flourish in the shade partition relatively more nitrogen into the thylakoids, although this is associated with lower photosynthetic capacity per unit of nitrogen.
TL;DR: A critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ≥ 5 cm diameter, directly harvested in 27 study sites across the tropics, is provided.
Abstract: Tropical forests hold large stores of carbon, yet uncertainty remains regarding their quantitative contri- bution to the global carbon cycle. One approach to quantifying carbon biomass stores consists in inferring changes from long-term forest inventory plots. Regres- sion models are used to convert inventory data into an estimate of aboveground biomass (AGB). We provide a critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ‡ 5 cm diameter, directly harvested in 27 study sites across the tropics. Proportional rela- tionships between aboveground biomass and the prod- uct of wood density, trunk cross-sectional area, and total height are constructed. We also develop a regres- sion model involving wood density and stem diameter only. Our models were tested for secondary and old- growth forests, for dry, moist and wet forests, for low- land and montane forests, and for mangrove forests. The most important predictors of AGB of a tree were, in decreasing order of importance, its trunk diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). Overestimates prevailed, giving a bias of 0.5-6.5% when errors were averaged across all stands. Our regression models can be used reliably to predict aboveground tree biomass across a broad range of tropical forests. Because they are based on an unprece- dented dataset, these models should improve the quality
TL;DR: Rooting patterns for terrestrial biomes are analyzed and distributions for various plant functional groups are compared and the merits and possible shortcomings of the analysis are discussed in the context of root biomass and root functioning.
Abstract: Understanding and predicting ecosystem functioning (e.g., carbon and water fluxes) and the role of soils in carbon storage requires an accurate assessment of plant rooting distributions. Here, in a comprehensive literature synthesis, we analyze rooting patterns for terrestrial biomes and compare distributions for various plant functional groups. We compiled a database of 250 root studies, subdividing suitable results into 11 biomes, and fitted the depth coefficient β to the data for each biome (Gale and Grigal 1987). β is a simple numerical index of rooting distribution based on the asymptotic equation Y=1-βd, where d = depth and Y = the proportion of roots from the surface to depth d. High values of β correspond to a greater proportion of roots with depth. Tundra, boreal forest, and temperate grasslands showed the shallowest rooting profiles (β=0.913, 0.943, and 0.943, respectively), with 80-90% of roots in the top 30 cm of soil; deserts and temperate coniferous forests showed the deepest profiles (β=0.975 and 0.976, respectively) and had only 50% of their roots in the upper 30 cm. Standing root biomass varied by over an order of magnitude across biomes, from approximately 0.2 to 5 kg m-2. Tropical evergreen forests had the highest root biomass (5 kg m-2), but other forest biomes and sclerophyllous shrublands were of similar magnitude. Root biomass for croplands, deserts, tundra and grasslands was below 1.5 kg m-2. Root/shoot (R/S) ratios were highest for tundra, grasslands, and cold deserts (ranging from 4 to 7); forest ecosystems and croplands had the lowest R/S ratios (approximately 0.1 to 0.5). Comparing data across biomes for plant functional groups, grasses had 44% of their roots in the top 10 cm of soil. (β=0.952), while shrubs had only 21% in the same depth increment (β=0.978). The rooting distribution of all temperate and tropical trees was β=0.970 with 26% of roots in the top 10 cm and 60% in the top 30 cm. Overall, the globally averaged root distribution for all ecosystems was β=0.966 (r 2=0.89) with approximately 30%, 50%, and 75% of roots in the top 10 cm, 20 cm, and 40 cm, respectively. We discuss the merits and possible shortcomings of our analysis in the context of root biomass and root functioning.
TL;DR: This work is a review of the up-to-date literature dealing with changes imposed by fires on properties of forest soils, and ecological implications of these changes are described.
Abstract: Many physical, chemical, mineralogical, and biological soil properties can be affected by forest fires. The effects are chiefly a result of burn severity, which consists of peak temperatures and duration of the fire. Climate, vegetation, and topography of the burnt area control the resilience of the soil system; some fire-induced changes can even be permanent. Low to moderate severity fires, such as most of those prescribed in forest management, promote renovation of the dominant vegetation through elimination of undesired species and transient increase of pH and available nutrients. No irreversible ecosystem change occurs, but the enhancement of hydrophobicity can render the soil less able to soak up water and more prone to erosion. Severe fires, such as wildfires, generally have several negative effects on soil. They cause significant removal of organic matter, deterioration of both structure and porosity, considerable loss of nutrients through volatilisation, ash entrapment in smoke columns, leaching and erosion, and marked alteration of both quantity and specific composition of microbial and soil-dwelling invertebrate communities. However, despite common perceptions, if plants succeed in promptly recolonising the burnt area, the pre-fire level of most properties can be recovered and even enhanced. This work is a review of the up-to-date literature dealing with changes imposed by fires on properties of forest soils. Ecological implications of these changes are described.
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