Spatial autocorrelation and sampling design in plant ecology

Using spatial analysis methods such as spatial autocorrelation coefficients (Moran's I and Geary's c) and kriging, we compare the capacity of different sampling designs and sample sizes to detect the spatial structure of a sugar-maple (Acer saccharum L.) tree density data set gathered from a secondary growth forest of southwestern Québec. Three different types of subsampling designs (random, systematic and systematic-cluster) with small sample sizes (50 and 64 points), obtained from this larger data set (200 points), are evaluated. The sensitivity of the spatial methods in the detection and the reconstruction of spatial patterns following the application of the various subsampling designs is discussed. We find that the type of sampling design plays an important role in the capacity of autocorrelation coefficients to detect significant spatial autocorrelation, and in the ability to accurately reconstruct spatial patterns by kriging. Sampling designs that contain varying sampling steps, like random and systematic-cluster designs, seem more capable of detecting spatial structures than a systematic design.Abbreviations UPGMA = Unweighted Pair-Group Method using Arithmetic Averages     

Ecotones in vegetation ecology: methodologies and definitions revisited

In this review paper, we highlight some of the trends and issues that have dominated ecotone research over the decade of 1996–2006. The terms and definitions of ecotone research in vegetation ecology are reviewed. We summarize the most important techniques and highlight the discrepancies between the definitions and their scientific application in vegetation ecology. We see a need for semantic uniformity with regard to the term and the definition of “the ecotone”, as the variable and the non-exclusive use of terms and definitions can be a source of confusion when interpreting and comparing different studies. To avoid further confusion, a unique definition of the term “ecotone” should be agreed upon, based upon a set of general characteristics. We therefore adapted and extended the definition from Holland et al. (Ecotones: the role of landscape boundaries in the management and restoration of changing environments, 1991) to “A multi-dimensional environmentally stochastic interaction zone between ecological systems with characteristics defined in space and time, and by the strength of the interaction”. We also advocate that (1) a shift in focus from one-dimensional to two-dimensional techniques in ecotone characterization is desirable and (2) more research into novel techniques, including multi-dimensional data and time series, is needed in view of local and global ecotone changes.     

The biology and ecology of tropical marine sardines and herrings in Indo-West Pacific fisheries: a review

Reviews in Fish Biology and Fisheries - Tropical sardines and herrings (Family Clupeidae) form important fisheries resources in the Indo-West Pacific region. However knowledge of their biology and...     

Metacommunity ecology meets biogeography: effects of geographical region,spatial dynamics and environmental filtering on community structure in aquatic organisms

Metacommunity patterns and underlying processes in aquatic organisms have typically been studied within a drainage basin. We examined variation in the composition of six freshwater organismal groups across various drainage basins in Finland. We first modelled spatial structures within each drainage basin using Moran eigenvector maps. Second, we partitioned variation in community structure among three groups of predictors using constrained ordination: (1) local environmental variables, (2) spatial variables, and (3) dummy variable drainage basin identity. Third, we examined turnover and nestedness components of multiple-site beta diversity, and tested the best fit patterns of our datasets using the “elements of metacommunity structure” analysis. Our results showed that basin identity and local environmental variables were significant predictors of community structure, whereas within-basin spatial effects were typically negligible. In half of the organismal groups (diatoms, bryophytes, zooplankton), basin identity was a slightly better predictor of community structure than local environmental variables, whereas the opposite was true for the remaining three organismal groups (insects, macrophytes, fish). Both pure basin and local environmental fractions were, however, significant after accounting for the effects of the other predictor variable sets. All organismal groups exhibited high levels of beta diversity, which was mostly attributable to the turnover component. Our results showed consistent Clementsian-type metacommunity structures, suggesting that subgroups of species responded similarly to environmental factors or drainage basin limits. We conclude that aquatic communities across large scales are mostly determined by environmental and basin effects, which leads to high beta diversity and prevalence of Clementsian community types.     

Colonization rules and spatial distributions in ecology

Because they are intuitive and mathematically straight-forward, colonization rules are often used to model spatial patterns in ecology. Colonization rules assign individuals to categories according to the locations of previous colonists. In this note, a compact introduction to colonization rules in ecology is presented with implications for autocorrelation and spatial distributions. I use the colonization rule approach to unify a diverse set of spatial and species diversity analyses, exploring future extensions to incorporate greater realism.     

Bias in genetic variance estimates due to spatial autocorrelation

Summary A central problem in the analysis of genetic field trials is the dichotomy of genetic and environmental effects because one cannot be defined without the other. Results from 768,000 simulated family trials in complete randomized block designs demonstrated a serious upward bias in estimates of family variance components from multi-unit plot designs when the phenotypic observations were compatible with a first-order autoregressive (AR1) process. The inflation of family variances and, thus, additive genetic variance and narrow sense individual heritabilities progressed exponentially with an increase in the nearest neighbor correlation () in the AR1 process. Significant differences in inflation rates persisted among various plot configurations. At = 0.2 the inflation of family variances reached 48–73%. Inflation rates were independent of the level of heritability. Modified Papadakis nearest neighbor (NN) adjustment procedures were tested for their ability to remove the bias in family variances. A NN-adjustment based on Mead's coefficient of interplant interaction and one derived from Bartlett's simultaneous autoregressive scheme removed up to 97% of the bias introduced by the phenotypic correlations. NN-adjusted estimates had slightly (5–8%) higher relative errors than did unadjusted estimates.     

Identifiability: the first step in parameter estimation

The observations in an experiment define a set of observational parameters that are functions of the basic kinetic parameters of the model of the system. The problem of identifiability is concerned with whether the observational parameters uniquely specify the basic kinetic parameters. As such, it depends only on the functional relation between the two levels of parameters and not on errors of observation and the estimation procedure. It should be checked before doing the experiment. Given initial estimates of the basic kinetic parameters, identifiability can be checked, in a local sense, from data generated by simulating the experiment on the model.     

Mast-producing trees and the geographical ecology of western scrub-jays

We analyzed the relationship between population abundance and variability of western scrub-jays Aphelocoma californica based on 48 yr of Audubon Christmas Bird Counts and the resources on which they depend as indexed by the diversity and abundance of mast-producing oaks and pines and, for California, estimates of acorn production based on a statewide survey. In general, populations of A. c. californica along the Pacific coast were related to oaks, with populations being more abundant and more stable in areas with more oak species and greater oak abundance. In contrast, populations of A. c. woodhouseii in the Great Basin were correlated with pines/conifers, again with higher abundance and greater stability with increased number of pine species and greater abundance of pines/conifers. The presumed driver of these patterns is increased resource abundance with greater habitat abundance and increased resource stability with increasing species diversity due to asynchrony in seed production among different species of trees. Asynchrony in acorn production is particularly high among oaks that require different number of years to produce acorns, but we failed to confirm that populations with access to both types were more stable than those with access to only one type after controlling for oak diversity. However, we did find a strong positive correlation between overall mean scrub-jay abundance in California and overall acorn production one year earlier, suggesting that acorns benefit scrub-jay populations primarily by increasing reproductive success the following year. These patterns demonstrate the strong dependence between population dynamics and resource stability as well as how different these relationships can be within closely related taxa.     

Evaluating spatial autocorrelation and depletion in pitfall-trap studies of environmental gradients

Studies of environmental gradients like edge effects commonly employ designs where samples are collected at unequal distances within transects. This approach risks confounding species patterns caused by the environmental gradient with patterns resulting from the spatial arrangement of the sampling scheme. Spatial autocorrelation and depletion (reduced catch) have the potential to influence pitfall-trap collections of invertebrates. Readily available control data from a study of edge and riparian effects on forest litter beetles was used to assess autocorrelation and depletion effects. Data from control transects distant from the treatment transects located at habitat edges and streams were screened to determine whether the study design (pitfall traps at varying distances within transects) was imposing patterns on the data attributable to differential autocorrelation or depletion. Autocorrelation in species composition and assemblage structure was not detected within the 99 m transects. The abundance and species richness of beetles were not lower where traps were in closer proximity, indicating that the transect design was not causing measurable depletion or resulting in differential trap catch. These findings indicate that spatial autocorrelation and depletion are unlikely to impair further analyses of edge and riparian effects on litter beetles.     

城市景观空间自相关与自相似的尺度特征研究

在RS与GIS技术支持下,以上海市外环以内城市中心区为区域背景,研究城市景观格局空间自相关与分形结构在不同粒度下的变化特征,揭示其对粒度的响应特征和敏感程度。结果表明,城市景观格局的空间自相关性和自相似性依赖于粒度的选择,MoranⅠ与各种景观类型的分维数均存在尺度效应;城市景观格局空间自相关性与自相似性在一定尺度范围内具有尺度依赖性,但二者对尺度的依赖程度不同。空间自相关性具有较高的尺度依赖性;而不同景观类型的自相似性随尺度的变化没有统一规律;城市景观格局的空间自相关性和自相似性对尺度变化的敏感点不同。居住景观的MoranⅠ对尺度变化的敏感点为50m,其他景观类型无明显的敏感点;分维数对尺度的敏感点因景观类型不同而异。     

Genetic microstructure in two Spanish cat populations. II: gametic disequilibrium and spatial autocorrelation

In a previous publication, we described some aspects of the microgenetic structure of two Spanish cat populations (in Barcelona and Alicante). In the present study, the possible existence ofgametic disequilibrium and spatial genetic structure for these populations, at the coat colour pattern and length genes O, A, T D, L, S and W, was analyzed. There was little gametic disequilibrium between pairs of these loci, despite certain pairs that showed significant systematic gametic disequilibrium (a-d and O-S), which appears to show the action of natural selection on domestic cat populations. Nevertheless, we believe that the major cause of the small amount of gametic disequilibrium found was probably a combination of gene drift and gene flow. The results obtained here were clearly in disagreement with those of Hedrick (1985), who concluded that epistatic selection was the cause of the gametic disequilibrium that he found in cat populations. We also found that although Hardy-Weinberg equilibrium could not be demonstrated, the gametic disequilibrium statistics were not affected by this fact, adding credence to the estimates obtained. We found no genetic spatial structure inside the city of Barcelona, as shown by analysis of the spatial autocorrelation of the individual loci, and analysis of the coordinates of the two first axes of a multidimensional scale. However, some gametic disequilibrium statistics showed certain spatial patterns, which leads us to consider the possibility of several evolutionary processes acting upon some of Barcelona's cat colonies.     

Detecting spatial hot spots in landscape ecology

Hot spots are typically locations of abundant phenomena. In ecology, hot spots are often detected with a spatially global threshold, where a value for a given observation is compared with all values in a data set. When spatial relationships are important, spatially local definitions – those that compare the value for a given observation with locations in the vicinity, or the neighbourhood of the observation – provide a more explicit consideration of space. Here we outline spatial methods for hot spot detection: kernel estimation and local measures of spatial autocorrelation. To demonstrate these approaches, hot spots are detected in landscape level data on the magnitude of mountain pine beetle infestations. Using kernel estimators, we explore how selection of the neighbourhood size (τ) and hot spot threshold impact hot spot detection. We found that as τ increases, hot spots are larger and fewer; as the hot spot threshold increases, hot spots become larger and more plentiful and hot spots will reflect coarser scale spatial processes. The impact of spatial neighbourhood definitions on the delineation of hot spots identified with local measures of spatial autocorrelation was also investigated. In general, the larger the spatial neighbourhood used for analysis, the larger the area, or greater the number of areas, identified as hot spots.     

On parameter estimation in population models

We describe methods for estimating the parameters of Markovian population processes in continuous time, thus increasing their utility in modelling real biological systems. A general approach, applicable to any finite-state continuous-time Markovian model, is presented, and this is specialised to a computationally more efficient method applicable to a class of models called density-dependent Markov population processes. We illustrate the versatility of both approaches by estimating the parameters of the stochastic SIS logistic model from simulated data. This model is also fitted to data from a population of Bay checkerspot butterfly (Euphydryas editha bayensis), allowing us to assess the viability of this population.