Home About us Contact
|
Home About us Contact | ||
|
|
||
Species Abundance Data (species + abundance_data)
Selected AbstractsMethods to account for spatial autocorrelation in the analysis of species distributional data: a reviewECOGRAPHY, Issue 5 2007Carsten F. Dormann Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix. [source] Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effectsJOURNAL OF BIOGEOGRAPHY, Issue 11 2004Nicolas Titeux Abstract Aim, To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data set. Location, The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods, Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ,pure' environmental variation, (b) spatially-structured environmental variation, (c) ,pure' spatial variation and (d) unexplained, non-spatial variation. Results, The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ,pure' spatial and spatially-structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one-third of the variation for 12 species. Main conclusions, The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ,noise' in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all-scale spatial components of this variation (the source of ,false correlation') and to bring out the ,pure' environmental variation for ecological interpretation. [source] Spatial and temporal analysis of vegetation mosaics for conservation: poor fen communities in a Cornish valley mireJOURNAL OF BIOGEOGRAPHY, Issue 9 2003E. J. Southall Abstract Aim Biogeographers increasingly realize the importance of seeing plant communities as spatial mosaics and understanding the spatial and temporal heterogeneity of a site is often a key to successful conservation. The aim of this paper is to examine the approaches to the description and analysis of spatial and temporal variation in sub-communities within patch mosaics of vegetation in order to inform conservation management. The activities of the tin streaming industry in Cornwall over the last century have created a highly varied mosaic of poor fen vegetation on Goss Moor National Nature Reserve (NNR). The wetland mosaics comprise dry hummocks and different sized wet pools. The size and depth of the pools determines the rate and type of vegetation that develops, as does the nature of boundary or edge. The ergodic hypothesis is used to describe the various plant sub-communities and their boundaries to identify pathways of hydroseral succession. A further aim was to test the use of Ellenberg Indicator (EI) values as a tool for the rapid description of spatial and temporal environmental change on wetland sites with a view to their management. Location Goss Moor National Nature Reserve, Cornwall, UK. Methods An extensive survey of the whole wetland complex was undertaken to identify patches of poor fen vegetation containing Potentilla palustris (L.) Scop. and Menyanthes trifoliata L. At each patch, species abundance data were collected as well as associated environmental information such as depth of the organic layer and standing water depth, patch location, patch size and boundary type. The plant sub-communities present were defined using techniques of numerical classification [two-way indicator species analysis (twinspan)] and ordination [detrended correspondence analysis (DCA)] and these were ordered using the ergodic hypothesis in order to characterize the stages of the hydrosere. Floristic and environmental relationships were examined using canonical correspondence analysis (CCA). Further environmental differences between the poor fen sub-community types were characterized by weighted EI values for acidity (R), moisture (F), nitrogen (N) and light (L). Results and conclusions Twelve poor fen sub-community types were described and found to be distributed along a primary environmental gradient of organic matter depth, surface water height and bare substrate. Separation of the poor fen communities by a moisture gradient was considered as spatial evidence for hydroseral succession, which begins with the colonization of open-water pools created by tin excavations. High water levels were associated with the swamp communities, increased organic depth was associated with poor fen, and the type of boundary was shown to affect the resulting community composition. Weighted Community Ellenberg Indicator values for nitrogen, light, reaction and moisture are recommended as an effective tool for indicating differences between plant (sub-)communities. The importance of examining sub-community mosaics in the study of hydroseral development is stressed and the manner in which both sets of information may be used to underpin the conservation management of the site is demonstrated. [source] Mesoscale Patterns in the Floristic Composition of Forests in the Central Western Ghats of Karnataka, IndiaBIOTROPICA, Issue 4 2010B. R. Ramesh ABSTRACT We describe the mesoscale floristic patterns in the central Western Ghats of Karnataka, India, through combined analysis of woody species abundance and stand structure data from a network of ninety-six 1-ha sampling plots spread across 22,000 km2. A total of 61,906 individuals (,10 cm gbh) comprising 400 plant species from 254 genera and 75 families were recorded. Euphorbiaceae, Rubiaceae, Lauraceae and Moraceae families constituted 23.5 percent of the total number of species encountered. The relative dominance of species was skewed with Poecilonueron indicum, Xylia xylocarpa, Terminalia tomentosa and Anogeissus latifolia being dominant in some plots. Correspondence analysis (CA) and a nonmetric multidimensional scaling (NMDS) of plots by species abundances data showed similar arching patterns, with significant correlation between the first axis of CA and NMDS (r=0.77). Hierarchical clustering of plot scores along the three first CA axes resulted in splitting the plots into five different categories that broadly reflect the major bioclimatic features of the region. A multiscale bootstrapping test indicated that categorization of the wettest (wet evergreen group 1 and 2) and driest (dry deciduous) groups were robust (P<0.05 with 1000 bootstraps), while the remaining two transitional groups were uncertain (P=0.12 and 0.26 for moist deciduous and semi-evergreen group, respectively). Principal component analysis revealed that plots with similar floristic composition can encompass contrastingly different physiognomic structures (canopy cover, canopy height and mean tree diameter) probably in relation to their levels of disturbance. Observed patterns in the floristic composition have been discussed in the light of the complex interaction between the bioclimatic and disturbance regimes that characterize the region. [source] | ||||||||||