Journal of Plant Ecology 

About: Journal of Plant Ecology is an academic journal published by Oxford University Press. The journal publishes majorly in the area(s): Species richness & Population. It has an ISSN identifier of 1752-9921. Over the lifetime, 1180 publications have been published receiving 23272 citations. The journal is also known as: Plant ecology & JPE.

Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages

Abstract: Aims In ecology and conservation biology, the number of species counted in a biodiversity study is a key metric but is usually a biased underestimate of total species richness because many rare species are not detected. Moreover, comparing species richness among sites or samples is a statistical challenge because the observed number of species is sensitive to the number of individuals counted or the area sampled. For individual-based data, we treat a single, empirical sample of species abundances from an investigator-defined species assemblage or community as a reference point for two estimation objectives under two sampling models: estimating the expected number of species (and its unconditional variance) in a random sample of (i) a smaller number of individuals (multinomial model) or a smaller area sampled (Poisson model) and (ii) a larger number of individuals or a larger area sampled. For sample-based incidence (presence–absence) data, under a Bernoulli product model, we treat a single set of species incidence frequencies as the reference point to estimate richness for smaller and larger numbers of sampling units. Methods The first objective is a problem in interpolation that we address with classical rarefaction (multinomial model) and Coleman rarefaction (Poisson model) for individual-based data and with sample-based rarefaction (Bernoulli product model) for incidence frequencies. The second is a problem in extrapolation that we address with sampling-theoretic predictors for the number of species in a larger sample (multinomial model), a larger area (Poisson model) or a larger number of sampling units (Bernoulli product model), based on an estimate of asymptotic species richness. Although published methods exist for many of these objectives, we bring them together here with some new estimators under a unified statistical and notational framework. This novel integration of mathematically distinct approaches allowed us to link interpolated (rarefaction) curves and extrapolated curves to plot a unified species accumulation curve for empirical examples. We provide new, unconditional variance estimators for classical, individual-based rarefaction and for Coleman rarefaction, long missing from the toolkit of biodiversity measurement. We illustrate these methods with datasets for tropical beetles, tropical trees and tropical ants.

Remote sensing imagery in vegetation mapping: a review

Abstract: Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.

Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors

Abstract: Aims We aim to construct a comprehensive global database of litter decomposition rate (k value) estimated by surface floor litterbags, and investigate the direct and indirect effects of impact factors such as geographic factors (latitude and altitude), climatic factors (mean annual tempePlrature, MAT; mean annual precipitation, MAP) and litter quality factors (the contents of N, P, K, Ca, Mg and C:N ratio, lignin:N ratio) on litter decomposition. Methods We compiled a large data set of litter decomposition rates (k values) from 110 research sites and conducted simple, multiple regression and path analyses to explore the relationship between the k values and impact factors at the global scale. Important findings The k values tended to decrease with latitude (LAT) and lignin content (LIGN) of litter but increased with temperature, precipitation and nutrient concentrations at the large spatial scale. Single factor such as climate, litter quality and geographic variable could not explain litter decomposition rates well. However, the combination of total nutrient (TN) elements and C:N accounted for 70.2% of the variation in the litter decomposition rates. The combination of LAT, MAT, C:N and TN accounted for 87.54% of the variation in the litter decomposition rates. These results indicate that litter quality is the most important direct regulator of litter decomposition at the global scale. This data synthesis revealed significant relationships between litter decomposition rates and the combination of climatic factor (MAT) and litter quality (C:N, TN). The global-scale empirical relationships developed here are useful for a better understanding and modeling of the effects of litter quality and climatic factors on litter decomposition rates.

Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis

Abstract: Aims Beta diversity is the variation in species composition among sites in a geographic region. Beta diversity is a key concept for understanding the functioning of ecosystems, for the conservation of biodiversity and for ecosystem management. The present report describes how to analyse beta diversity from community composition and associated environmental and spatial data tables. Methods Beta diversity can be studied by computing diversity indices for each site and testing hypotheses about the factors that may explain the variation among sites. Alternatively, one can carry out a direct analysis of the community composition data table over the study sites, as a function of sets of environmental and spatial variables. These analyses are carried out by the statistical method of partitioning the variation of the diversity indices or the community composition data table with respect to environmental and spatial variables. Variation partitioning is briefly described herein. Important findings Variation partitioning is a method of choice for the interpretation of beta diversity using tables of environmental and spatial variables. Beta diversity is an interesting ‘currency’ for ecologists to compare either different sampling areas or different ecological communities cooccurring in an area. Partitioning must be based upon unbiased estimates of the variation of the community composition data table that is explained by the various tables of explanatory variables. The adjusted coefficient of determination provides such an unbiased estimate in both multiple regression and canonical redundancy analysis. After partitioning, one can test the significance of the fractions of interest and plot maps of the fitted values corresponding to these fractions.