Plant communities are generally spatially structured. Therefore, in order to enhance the interpretation of distance-dependent community patterns, spatially explicit measures of β-diversity are needed that, besides simple species turnover, are able to account for the rate at which biological similarity decays with increasing distance. We show that a multivariate semivariogram computed from species presence and absence data can be considered as a space-dependent alternative to existing definitions of β-diversity. To illustrate how the proposed method works, we used a classical data set from a second-growth piedmont hardwood forest.
Authors:C. M. Mutshinda, Z. V. Finkel, C. E. Widdicombe, and A. J. Irwin
Ecological communities are shaped by a complex interplay between abiotic forcing, biotic regulation and demographic stochasticity. However, community dynamics modelers tend to focus on abiotic forcing overlooking biotic interactions, due to notorious challenges involved in modeling and quantifying inter-specific interactions, particularly for species-rich systems such as planktonic assemblages. Nevertheless, inclusive models with regard to the full range of plausible drivers are essential to characterizing and predicting community response to environmental changes. Here we develop a Bayesian model for identifying, from in-situ time series, the biotic, abiotic and stochastic factors underlying the dynamics of species-rich communities, focusing on the joint biomass dynamics of biologically meaningful groups. We parameterize a multivariate model of population co-variation with an explicit account for demographic stochasticity, density-dependent feedbacks, pairwise interactions, and abiotic stress mediated by changing environmental conditions and resource availability, and work out explicit formulae for partitioning the temporal variance of each group in its biotic, abiotic and stochastic components. We illustrate the methodology by analyzing the joint biomass dynamics of four major phytoplankton functional types namely, diatoms, dinoflagellates, coccolithophores and phytoflagellates at Station L4 in the Western English Channel using weekly biomass records and coincident measurements of environmental covariates describing water conditions and potentially limiting resources. Abiotic and biotic factors explain comparable amounts of temporal variance in log-biomass growth across functional types. Our results demonstrate that effective modelling of resource limitation and inter-specific interactions is critical for quantifying the relative importance of abiotic and biotic factors.
Two series of studies are reported dealing with (1) psychophysical characteristics and (2) interactions of visceroceptive and somatosensoric information processing. The first studies characterized detection, graduation and localisation of visceral as compared to somatic stimuli. The second series investigated somatovisceral discrimination, masking, and summation at different levels of awareness. Methods: Distension of the sigmoid colon served as standard model. The visceral stimulus was applied by a balloon probe in the sigmoid colon, the external abdominal stimulus by a ring-shaped stimulator at two abdominal sites. A forced-choice-paradigm with two observation intervals was applied (multiple staircase) to estimate interactions between somatosensation and visceroception. Results: The visceral distension stimulus can be detected or discriminated correctly without conscious sensation. Visceral localization of stimuli requires conscious sensation. Combining visceral and somatic stimuli resulted in distinct elevation of visceral thresholds demonstrating somatosensory masking of the visceral stimulus. There are characteristic somato-visceral and viscero-somatic differences in masking and qualitative differences between implicit and explicit processing stages. Specific electrocortical reactions to visceral stimuli could be shown. Discussion: Visceroception is represented on the highest functional level as a fairly independent submodality of body perception. There are several hints that visceroception and protopathic somatic sensitivity follow the same major paths and comprise the same ontogenetic origin. Perceptual interactions are determined by modality and awareness and depend on the task. The role of implicit and explicit body perception considering the body self and its significance in the context of consiousness are discussed.
In complex, modern food webs, the analysis of pairwise interactions gives weak predictions of the behaviour of either single species or the whole community. Indirect effects call for explicit study and quantification. However, just as focusing only on pairwise interactions is incorrect, overemphasising the role of long, indirect pathways also seems to be unrealistic. Thus, a reasonable range of indirect trophic effects spreading through the food web is to be defined and quantified. I suggest a graph theoretical measure for quantifying this range, considering only network position (topology). I call this the trophic field of a species (or trophic group), recalling the idea that field theory could be a fruitful research programme in biology. Further, I propose a measure for the quantification of the indirect component of the trophic field. Finally, the use of introduced concepts and indices is illustrated by analysing the trophic flow network of the Schlei Fjord ecosystem (N. Germany).
Authors:D. Rocchini, L. Dadalt, L. Delucchi, M. Neteler, and M.W. Palmer
Due to the difficulties of field-based species data collection at wide spatial scales, remotely sensed spectral diversity has been advocated as one of the most effective proxies of ecosystem and species diversity. It is widely accepted that the relationship between species and spectral diversity is scale dependent. However, few studies have evaluated the impacts of scale on species diversity estimates from remote sensing data. In this paper we tested the species versus spectral relationship over very large scales (extents) with a varying spatial grain using floristic data of North America. Spectral diversity explained a low amount of variance while spatial extent of the sampling units (floras) explained a high amount of variance based on results from our variance partitioning analyses. This leads to the conclusion that spectral diversity must be carefully related to species diversity, explicitly taking into account potential area effects.
Many methods of cluster analysis do not explicitly account for correlation between attributes. In this paper we explicitly model any correlation using a single factor within each cluster: i.e., the correlation of atributes within each cluster is adequately described by a single component axis. However, the use of a factor is not required in every cluster. Using a Minimum Message Length criterion, we can determine the number of clusters and also whether the model of any cluster is improved by introducing a factor. The technique allows us to seek clusters which reflect directional changes rather than imposing a zonation constrained by spatial (and implicitly temporal) position. Minimal message length is a means of utilising Okham’s Razor in inductive analysis. The ‘best’ model is that which allows most compression of the data, which results in a minimal message length for the description. Fit to the data is not a sufficient criterion for choosing models because more complicated models will almost always fit better. Minimum message length combines fit to the data with an encoding of the model and provides a Bayesian probability criterion as a means of choosing between models (and classes of model). Applying the analysis to a pollen diagram from Southern Chile, we find that the introduction of factors does not improve the overall quality of the mixture model. The solution without axes in any cluster provides the most parsimonious solution. Examining the cluster with the best case for a factor to be incorporated in its description shows that the attributes highly loaded on the axis represent a contrast of herbaceous vegetation and dominant forests types. This contrast is also found when fitting the entire population, and in this case the factor solution is the preferred model. Overall, the cluster solution without factors is much preferred. Thus, in this case classification is preferred to ordination although more data are desirable to confirm such a conclusion.
Multi-band remotely sensed image data contain information on landscape pattern and temporal changes that are greatly underutilized in this technological era when monitoring of disturbance and ecological dynamics is increasingly important to address questions regarding sustainability of ecosystem health and climate change. Among the reasons for this loss of analytical opportunity are the inadequacy of methods for systematic extraction of pattern elements, incongruity between information paradigms for remote sensing and geographic information systems (GIS), and the sheer volume of remotely sensed image data when acquired regularly over time. Long-term cooperative landscape ecological investigations concerning habitat and change detection in conjunction with remote sensing and GIS have yielded a pattern-based approach to progressively segmenting images (PSI) that culminates in a doubly segmented image representation by sets of approximating signal vectors that serve as parsimonious proxies for pixel vectors. The coarser level of segmentation is entirely congruent with raster map structures for GIS, and yet mimics the appearance of an image display by colorization using information on typical spectral properties of segments contained in attribute tables. The components of the coarser representation as spatial segments constitute explicit elements of pattern at several levels. The explicit nature of these pattern elements enables spatial pattern matching for change detection that resolves difficulties with phenological variability and continuity of sensor configurations over time. Conversion to segmented representation can be applied to multi-temporal change indices so as to elicit longer-term patterns of change from temporal sequences of images. The finer level of segmentation for spectral detail enables restoration of image bands in the manner of a low-pass filter for analysis according to the usual paradigms of remote sensing. Mapping of the residuals for the finer detail of image approximation provides further information on exceptional features of landscape ecological pattern.
Biodiversity monitoring is important to identify conservation needs and test the efficacy of management actions. Variants of “abundance” (
) are among the most widely monitored quantities, e.g., (true) abundance, number of occupied sites (distribution, occupancy) or species richness. We propose a sampling-based view of monitoring that clearly acknowledges two sampling processes involved when monitoring
. First, measurements from the surveyed sample area are generalized to a larger area, hence the importance of a probability sample. Second, even within sampled areas only a sample of units (individuals, occupied sites, species) is counted owing to imperfect detectability
< 1, counts are random variables and their expectation
) is related to
via the relationship
< 1, counts vary even under identical conditions and underestimate
, and patterns in counts confound patterns in
with those in
. In addition, part of the population
may be unavailable for detection, e.g., temporarily outside the sampled quadrat, underground or for another reason not exposed to sampling; hence a more general way of describing a count is
is availability probability and
detection, given availability. We give two examples of monitoring schemes that highlight the importance of explicitly accounting for availability and detectability. In the Swiss reptile Red List update, the widespread and abundant slow worm (
) was recorded in only 22.1% of all sampled quadrats. Only an analysis that accounted for both availability and detectability gave realistic estimates of the species’ distribution. Among 128 bird species monitored in the Swiss breeding bird survey, detection in occupied 1 km
quadrats averaged only 64% and varied tremendously by species (3–99 %); hence observed distributions greatly underestimated range sizes and should not be compared among species. We believe that monitoring design and analyses should properly account for these two sampling processes to enable valid inferences about biodiversity. We argue for a more rigorous approach to both monitoring design and analysis to obtain the best possible information about the state of nature. An explicit recognition of, and proper accounting for, the two sampling processes involved in most monitoring programs will go a long way towards this goal.