On the perceived location of global motion

We measured the effects of coherent motion of one set of dots on the perceived location of Gaussian envelopes formed by luminance modulation of a second set of dots. Perceived shifts in envelope location in the direction of coherent motion were obtained even when the dots forming the envelopes did not physically move in the direction of coherent motion. In such cases, perceived shifts coincided with stimulus configurations that permitted motion integration of the envelope dots with the coherently moving dots, for example, when envelope dots moved in random directions as opposed to being static. In subsequent experiments we explored the type of motion integration underlying the positional shifts obtained. We discounted the possibility that the visual system incorrectly attributes motion signals associated with coherently moving dots to envelope dots by demonstrating that positional shifts could be obtained even when the coherent dots were laterally displaced to either side of the envelope dots such that the regions occupied by the dots did not overlap. We also discounted spatio-temporal summation within the receptive fields of low-spatial-frequency motion-sensitive mechanisms by demonstrating that positional shifts persisted even when the dot displays were high-pass filtered. These results, coupled with the observation that the proportion of coherently moving dots required to produce positional shifts correlated well with global motion thresholds measured for the same dot configurations, suggests that visual processes which underlie motion-dependent positional shifts are based at least in part on cooperative interactions of the type implicated in global motion.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Spatial turnover in the global avifauna

Despite its wide implications for many ecological issues, the global pattern of spatial turnover in the occurrence of species has been little studied, unlike the global pattern of species richness. Here, using a database on the breeding distributions of birds, we present the first global maps of variation in spatial turnover for an entire taxonomic class, a pattern that has to date remained largely a matter of conjecture, based on theoretical expectations and extrapolation of inconsistent patterns from different biogeographic realms. We use these maps to test four predictions from niche theory as to the form that this variation should take, namely that turnover should increase with species richness, towards lower latitudes, and with the steepness of environmental gradients and that variation in turnover is determined principally by rare (restricted) species. Contrary to prediction, we show that turnover is high both in areas of extremely low and high species richness, does not increase strongly towards the tropics, and is related both to average environmental conditions and spatial variation in those conditions. These results are closely associated with a further important and novel finding, namely that global patterns of spatial turnover are driven principally by widespread species rather than the restricted ones. This complements recent demonstrations that spatial patterns of species richness are also driven principally by widespread species, and thus provides an important contribution towards a unified model of how terrestrial biodiversity varies both within and between the Earth's major land masses.

Linking pesticide exposure and spatial dynamics: An individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes

The wood mouse is a common and abundant species in agricultural landscape and is a focal species in pesticide risk assessment. Empirical studies on the ecology of the wood mouse have provided sufficient information for the species to be modelled mechanistically. An individual-based model was constructed to explicitly represent the locations and movement patterns of individual mice. This together with the schedule of pesticide application allows prediction of the risk to the population from pesticide exposure. The model included life-history traits of wood mice as well as typical landscape dynamics in agricultural farmland in the UK. The model obtains a good fit to the available population data and is fit for risk assessment purposes. It can help identify spatio-temporal situations with the largest potential risk of exposure and enables extrapolation from individual-level endpoints to population-level effects. Largest risk of exposure to pesticides was found when good crop growth in the “sink” fields coincided with high “source” population densities in the hedgerows. Keywords: Population dynamics, Pesticides, Ecological risk assessment, Habitat choice, Agent-based model, NetLogo

Umbrella pine above ground biomass estimation with very high spatial resolution satellite images

Above ground biomass is frequently estimated with forest inventory data and an extrapolation method for the per unit area evaluations. This procedure is labour demanding and costly. In this study above ground biomass functions, whose independent variable is crown horizontal projection, were developed. Multi-resolution segmentation method and object-oriented classification, based on very high spatial resolution satellite images, were used to obtain the area of tree crown horizontal projection for umbrella pine (Pinus pinea L.). A set of inventory plots were measured and with existing allometric functions for this species above ground biomass per tree and per plot were calculated. The two data sets were used to fit linear functions both for individual plot and their cumulative values. The results show a good performance of the models. Errors smaller than 10% are obtained for stand areas greater than 1.4 ha. These functions have the advantages of estimating above ground biomass for all the area under study or surveillance, not requiring forest inventory; allow monitoring in short time periods; and are easily implemented in a geographical information system environment.

Above ground biomass estimation in Mediterranean multiple use systems with high spatial resolution satellite images

Forest biomass has been having an increasing importance in the world economy and in the evaluation of the forests development and monitoring. It was identified as a global strategic reserve, due to its applications in bioenergy, bioproduct development and issues related to reducing greenhouse gas emissions. The estimation of above ground biomass is frequently done with allometric functions per species with plot inventory data. An adequate sampling design and intensity for an error threshold is required. The estimation per unit area is done using an extrapolation method. This procedure is labour demanding and costly. The mail goal of this study is the development of allometric functions for the estimation of above ground biomass with ground cover as independent variable, for forest areas of holm aok (Quercus rotundifolia), cork oak (Quercus suber) and umbrella pine (Pinus pinea) in multiple use systems. Ground cover per species was derived from crown horizontal projection obtained by processing high resolution satellite images, orthorectified, geometrically and atmospheric corrected, with multi-resolution segmentation method and object oriented classification. Forest inventory data were used to estimate plot above ground biomass with published allometric functions at tree level. The developed functions were fitted for monospecies stands and for multispecies stands of Quercus rotundifolia and Quercus suber, and Quercus suber and Pinus pinea. The stand composition was considered adding dummy variables to distinguish monospecies from multispecies stands. The models showed a good performance. Noteworthy is that the dummy variables, reflecting the differences between species, originated improvements in the models. Significant differences were found for above ground biomass estimation with the functions with and without the dummy variables. An error threshold of 10% corresponds to stand areas of about 40 ha. This method enables the overall area evaluation, not requiring extrapolation procedures, for the three species, which occur frequently in multispecies stands.