Patterns in the distribution of leaf-miners on British trees

Abstract. 1. A multiple regression analysis of the leaf miners on British native angiosperm tree genera was performed using measures of plant range, maximum height, taxonomic isolation and species per genus as independent variables.
2. 69% of the variation was explained by regression.
3. Taxonomic isolation was the most important variable with species per genus also having a significant effect. Plant range had no demonstrable effect.
4. The analysis was repeated with component taxonomic groups of leaf miners. Up to 85% of the variation was explained.     

Diversity of ectomycorrhizal fungi in Britain: a test of the species–area relationship, and the role of host specificity

The host range of ectomycorrhizal (ECM) fungi in Britain was examined by compilation of a data matrix from published literature sources, based primarily on accounts of sporocarp associations with particular host genera. Information was gathered for 577 species of ECM fungi belonging to 51 genera, and 25 genera of host trees, representing the majority of ECM fungal species and host genera recorded in Britain.
Pronounced variation was recorded in the number of ECM fungal species associated with different host genera, with over 200 species recorded with Betula , Fagus , Pinus and Quercus . There was a positive linear relationship ( r ²=0·47, P =0·007) between the number of species of ECM fungi associated with different host genera and the total area occupied by each tree genus in Britain (both values log-transformed). There was also variation in the number of species of ECM fungi which were apparently specific to particular host genera, values ranging from zero (in 15 genera) to >40 in the case of Betula and Fagus . In total, 233 fungal species appeared to be specific to a single host genus (i.e. 40% of those surveyed). Comparison of the ECM mycota associated with different host genera by PCA accounted for 17% of the total variation, with genera belonging to the Fagaceae ( Quercus , Fagus and Castanea ) tending to cluster together, indicating a degree of overlap in their ECM associates. Exotic conifer species, which displayed a lower ECM diversity than would be expected from their distributional areas, were characterized by a high degree of overlap with the ECM associates of Pinus and Betula .
These results indicate that the abundance of different genera of host trees and variation in host specificity could provide a basis for understanding patterns of diversity in ECM fungi within Britain.     

Species‐time‐area and phylogenetic‐time‐area relationships in tropical tree communities

The species‐area relationship (SAR) has proven to be one of the few strong generalities in ecology. The temporal analog of the SAR, the species‐time relationship (STR), has received considerably less attention. Recent work primarily from the temperate zone has aimed to merge the SAR and the STR into a synthetic and unified species‐time‐area relationship (STAR) as originally envisioned by Preston (1960). Here we test this framework using two tropical tree communities and extend it by deriving a phylogenetic‐time‐area relationship (PTAR). The work finds some support for Preston's prediction that diversity‐time relationships, both species and phylogenetic, are sensitive to the spatial scale of the sampling. Contrary to the Preston's predictions we find a decoupling of diversity‐area and diversity‐time relationships in both forests as the time period used to quantify the diversity‐area relationship changes. In particular, diversity‐area and diversity‐time relationships are positively correlated using the initial census to quantify the diversity‐area relationship, but weakly or even negatively correlated when using the most recent census. Thus, diversity‐area relationships could forecast the temporal accumulation of biodiversity of the forests, but they failed to “back‐cast” the temporal accumulation of biodiversity suggesting a decoupling of space and time.     

Dispersal and establishment: spatial patterns and species–area relationships

According to the equilibrium theory of island biogeography, high colonization ability of species is associated with low exponents (z) of the species–area relationship (SAR) and weak spatial patterns in species number and dissimilarity. However, the relationship between z and the strength of these spatial patterns has not been investigated systematically. We used a multispecies metapopulation model to investigate these relationships in an archipelago of islands. We conclude that this relationship can only be predicted if either the dispersal ability or the power of establishment of species is known. With species richness limited by establishment, we generated high z‐values associated with weak spatial patterns in species number and dissimilarity. If species richness was constrained by the dispersal ability of species, we observed low to medium z‐values but strong spatial patterns. If the dispersal ability and the abilities of species to establish were both high, z‐values and spatial pattern tend to be low and species numbers were limited by the size of the regional species pool.     

小麦叶面积指数与冠层反射光谱的定量关系

在分析不同氮素水平下小麦叶面积指数（LAI）和冠层光谱反射率随生育期变化模式的基础上,确立了LAI与冠层光谱反射率及光谱参数的相关关系,提出了小麦LAI的敏感光谱参数及预测方程.结果表明,小麦LAI和近红外短波段（760～1 220 nm）反射率都随施氮量的增加呈上升趋势,可见光波段反射率则相反;从拔节期到成熟期,LAI和近红外短波段反射率均表现为先上升后下降的趋势,而可见光波段（460～710 nm）反射率随生育期的推进先降低后升高,以孕穗期反射率最低,近红外长波段区域（1 480～1 650 nm）反射率的变化与可见光部分相同.LAI与可见光波段反射率呈负相关,与近红外短波段反射率呈极显著正相关,其中以810 nm相关性最好.可以选择RVI(810,510)和DVI(810,560)作为反演小麦LAI的光谱参数.另外,在证明垂直植被指数PVI和转换型土壤调整指数TSAVI对LAI预测能力的同时,发现利用RVI(810,510)、DVI(810,560)和PVI 3个植被指数共同推算小麦LAI的准确度更高.     

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species‐level Poisson processes and estimate patch‐level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early‐successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.     

The terminology and use of species–area relationships: a response to Dengler (2009)

Dengler ( Journal of Biogeography , 2009, 36 , 728–744) addresses issues regarding species–area relationships (SARs), but fails to settles those issues. He states that only certain types of sampling schemes should be used to construct SARs, but is not consistent in the criteria that he uses to include some sampling schemes but not others. He argues that a sampling scheme of contiguous plots will be more accurate in extrapolating beyond the sampled area, but logic tells us that a dispersed sampling scheme is likely to be more accurate. Finally, he concludes that the 'true' SAR is a power function, but this conclusion is inconsistent with his results and with the results of others. Rather than defining a narrow framework for SARs, we need to recognize that the relationship between area and species richness is scale- and system-dependent. Different sampling schemes serve different purposes, and a variety of functional relationships are likely to hold. Further theoretical and empirical work is needed to resolve these issues fully.     

Global pattern and local variation in species–area relationships

Aim We conducted a meta‐analysis of species–area relationships (SARs) by combining several data sets and important covariates such as types of islands, taxonomic groups, latitude and spatial extent, in a hierarchical model framework to study global pattern and local variation in SARs and its consequences for prediction. Location One thousand nine hundred and eighteen islands from 94 SAR studies from around the world. Methods We developed a generalization of the power‐law SAR model, the HSARX model, which allows: (1) the inclusion of multiple focal parameters (intercept, slope, within‐study variance), (2) use of multiple effect modifiers based on a collection of SAR studies, and (3) modelling of the between‐ and within‐study variability. Results The global pattern in the SAR was the average of local SARs and had wide confidence intervals. The global SAR slope was 0.228 with 90% confidence limits of 0.059 and 0.412. The intercept, slope and within‐study variability of local SARs showed great heterogeneity as a result of the interaction of modifying covariates. Confidence intervals for these SAR parameters were narrower when other covariates in addition to area were accounted for, thus increasing the accuracy of the predictions for species richness. The significant effect of latitude and the interaction of latitude, taxa and island type on the SAR slope indicated that the ‘typical’ latitudinal diversity gradient can be reversed in isolated systems. Main conclusions The power‐law relationship underlying the HSARX model provides a good fit for non‐nested SARs across vastly different spatial scales by taking into account other covariates. The HSARX framework allows researchers to explore the complex interactions among SAR parameters and modifying variables, to explicitly study the scale dependence, and to make robust predictions on multiple levels (island, study, global) with associated prediction intervals. From a prediction perspective, it is not the global pattern but the local variation that matters.     

sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

东祁连山高寒草地柳灌丛群落种间关联性

采用联结系数和共同出现百分率,对东祁连山高寒地区柳灌丛群落种间关联性进行了研究。结果表明,柳灌丛群落内灌木植物种群间的关系表现为以下3种类型1)正联结。海拔3050m除坡柳与杯腺柳之间无关联外,其它种对均表现为正联结,海拔3050、3100、3200以及3250m处金露梅与高山绣线菊之间、海拔3150~3350m山生柳与金露梅之间、海拔3300、3350以及3450m处山生柳与头花杜鹃之间等,表现出正关联;2)负联结。海拔3100m处金露梅和川滇柳之间、海拔3150m处山生柳与坡柳以及金露梅和坡柳之间、海拔3450m处山生柳与杯腺柳之间、川滇柳和头花杜鹃之间,表现为负关联;3)无联结。海拔3050m坡柳与杯腺柳之间、海拔3100~3250m头花杜鹃与其它种群之间以及海拔3400m处除高山绣线菊与头花杜鹃之间外,各种群之间,无任何关联种对。