Effects of plot size on the ordination of vegetation samples

Questions: Do ordination patterns differ when based on vegetation samples recorded in plots of different size? If so, how large is the effect of plot size relative to the effects of data set heterogeneity and of using presence/absence or cover‐abundance data? Can we combine plots of different size in a single ordination? Methods: Two homogeneous and two heterogeneous data sets were sampled in Czech forests and grasslands. Cover‐abundances of plant species were recorded in series of five or six nested quadrats of increasing size (forest 49‐961 m2; grassland 1‐49 m²). Separate ordinations were computed for plots of each size for each data set, using either species presences/absences or cover‐abundances recorded on an ordinal scale. Ordination patterns were compared with Procrustean analysis. Also, ordinations of data sets jointly containing plots of different size were calculated; effects of plot size were evaluated using a Monte Carlo test in constrained ordination. Results: The results were consistent between forest and grassland data sets. In homogeneous data sets, the effect of presence/absence vs. cover‐abundance was similar to, or larger than, the effect of plot size; for presence/absence data the differences between ordinations of differently sized plots were smaller than for cover‐abundance data. In heterogeneous data sets, the effect of plot size was larger than the effect of presence‐absence vs. cover‐abundance. The plots of smaller size (= 100 m2 in forests, = 4 m² in grasslands) yielded the most deviating ordination patterns. Joint ordinations of differently sized plots mostly did not yield patterns that would be artifacts of different plot size, except for plots from the homogeneous data sets that differed in size by a factor of four or higher. Conclusions: Variation in plot size does influence ordination patterns. Smaller plots tend to produce less stable ordination patterns, especially in data sets with low ß‐diversity and species cover‐abundances. Data sets containing samples from plots of different sizes can be used for ordination if they represent vegetation with large ß‐diversity. However, if data sets are homogeneous, i.e. with low ß‐diversity, the differences in plot sizes should not be very large, in order to avoid the danger of plot size differences distorting the real vegetation differentiation in ordination patterns.     

Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta‐analysis

It is thought that species abundance is correlated with environmental suitability and that environmental variables, scale, and type of model fitting can confound this relationship. We performed a meta‐analysis to 1) test whether species abundance is positively correlated with environmental suitability derived from correlative ecological niche models (ENM), 2) test whether studies encompassing large areas within a species range (> 50%) exhibited higher AS correlations than studies encompassing small areas within a species range (< 50%), 3) assess which modelling method provided higher AS correlation, and 4) compare strength of the AS relationship between studies using only climatic variables and those that used both climatic and other environmental variables to derive suitability. We used correlation coefficients to measure the relationship between abundance and environmental suitability derived from ENM. Each correlation coefficient was considered an effect size in a random‐effects multivariate meta‐analysis. In all cases we found a significantly positive relationship between abundance and suitability. This relationship was consistent regardless of scale of study, ENM method, or set of variables used to derive suitability. There was no difference in strength of correlation between studies focusing on large or small areas within a species’ range or among ENM methods. Studies using other variables in combination with climate exhibited higher AS correlations than studies using only climatic variables. We conclude that occurrence data can be a reasonable proxy for abundance, especially for vertebrates, and the use of local variables increases the strength of the AS relationship. Use of ENMs can significantly decrease survey costs and allow the study of large‐scale abundance patterns using less information. Including only climatic variables in ENM may confound the relationship between abundance and suitability when compared to studies including variables taken locally. However, modelers and conservationists must be aware that high environmental suitability does not always indicate high abundance.     

Stability of the Wabash River fish assemblages from 1974 to 1998

1. We measured temporal variability of the fish assemblage in the Wabash River using a 25‐year electrofishing dataset through a combination of a time‐lag analysis and multivariate analysis. 2. The fish assemblage had substantial year‐to‐year variation, but the overall abundance pattern did not change. Based on time‐lag analyses and ordinations, there was only weak predictable directional change at individual sites. At the scale of the entire reach, however, the overall combined data had a statistically significant directionality with the time‐lag analysis. 3. These results support the hypothesis that the middle Wabash River stream fish assemblage has undergone change over a time period of 25 years, although at the scale of individual sites this change was not detectable.     

On the evaluation of ordinal data with conventional multivariate procedures

In a recent Forum paper, it is argued that, in most studies, ordinal data such as the Braun‐Blanquet abundance/dominance scale are not properly treated by multivariate methods. This is because conventional multivariate methods are generally adequate for ratio‐scale variables only, while for ordinal variables differences between states and their ratios are not interpreted. Conversely, in this paper it is shown that using conventional multivariate procedures for evaluating ordinal data should imply a shift from a metric space to a topological data space; as such the use of ordinal data does not represent a serious methodological error, provided that results are interpreted accordingly.     

Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real‐time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration‐related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1586–1599, 2015     

Coral and fish distribution patterns in Mafia Island Marine Park, Tanzania: fish–habitat interactions

We examined coral reef communities at 11 sites within Mafia Island Marine Park using a point count method for substrate and visually censused belt transects for fish populations. Multivariate ordinations showed that the benthic habitat differed among reefs. The patterns were mainly attributed to variations in depth, hydrodynamics and benthic composition. In total, the substratum was dominated by dead coral (49%) and algae (25%), with a live coral cover of only 14%. Three hundred and ninety-four fish species belonging to 56 families were recorded. According to MDS-ordinations and RELATE procedures, fish assemblage composition varied among sites in concordance with the habitats provided. Sites with highest proportion of dead coral exhibited highest degree of dispersion in the multivariate ordinations of fish assemblages. Stepwise multiple regression was used to determine the proportion of variance among sites which could be explained by depth, exposure, rugosity, substrate diversity, branching substrate, live coral cover, dead coral cover and different types of algae. The results showed that habitat variables explained up to 92% of the variation in species numbers and in total, and taxon-specific, abundance. Live coral cover was the foremost predictor of both numerical and species abundance, as well as of corallivores, invertivores, planktivores and of the families Pomacentridae, Chaetodontidae and Pomacanthidae. Our results suggest that habitat characteristics play a dominant role in determining fish assemblage composition on coral reefs.     

Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation

Question: Are there spatial structures in the composition of plant communities? Methods: Identification and measurement of spatial structures is a topic of great interest in plant ecology. Univariate measurements of spatial autocorrelation such as Moran's I and Geary's c are widely used, but extensions to the multivariate case (i.e. multi‐species) are rare. Here, we propose a multivariate spatial analysis based on Moran's I (MULTISPATI) by introducing a row‐sum standardized spatial weight matrix in the statistical triplet notation. This analysis, which is a generalization of Wartenberg's approach to multivariate spatial correlation, would imply a compromise between the relations among many variables (multivariate analysis) and their spatial structure (autocorrelation). MULTISPATI approach is very flexible and can handle various kinds of data (quantitative and/or qualitative data, contingency tables). A study is presented to illustrate the method using a spatial version of Correspondence Analysis. Location: Territoire d'Etude et d'Expérimentation de Trois‐Fontaines (eastern France). Results: Ordination of vegetation plots by this spatial analysis is quite robust with reference to rare species and highlights spatial patterns related to soil properties.     

A pathway for multivariate analysis of ecological communities using copulas

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity‐based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over‐dispersion, and/or zero‐inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation‐based filter with exact family‐wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity‐based settings. Our proposed analysis pathway provides new opportunities to combine model‐based approaches with dissimilarity‐based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.     

A numerical analysis of the mesoscale distribution patterns of vascular plants in the subarctic Kevo Nature Reserve, northern Finland

Different numerical techniques were used to detect and describe the major ecological-biogeographical patterns of vascular plant distributions at the meso-scale level in a subarctic region in Finland. The distribution patterns of 231 native taxa in 362 1 km² grid squares of the Kevo Nature Reserve were analysed by two-way indicator species analysis and detrended correspondence analysis, and were subsequently related to twenty-eight geographical, topographical, geological, and vegetational variables using simple discriminant functions and canonical correspondence analysis with associated Monte Carlo permutation tests.
The floristic variation detected reflects variations in environmental factors operative at the regional and local scales. No major broad-scale coherent geographical patterns were detected; instead, the spatial distribution of the grids with a similar floristic composition shows a scattered distribution. All the numerical techniques reveal a major gradient from alpine areas to lowland sites with rivers and rocky outcrops, and the most important explanatory variables for predicting the main floristic variation are all associated with altitude. The floristic patterns represented by the second ordination gradient mainly correlate with the abundance of mires. Partial ordinations indicate that both the geographical and geological variables explain relatively little of the species distributional patterns.
Although the meso-scale approach reveals much about the plant-environment relationships in the study area, the floristic variation appears to be determined mainly by fine-scale factors. In the most heterogeneous grids, the grid size used fails to detect accurately the ecological patterns of the species present.     

Sequence clustering threshold has little effect on the recovery of microbial community structure

Analysis of microbial community structure by multivariate ordination methods, using data obtained by high‐throughput sequencing of amplified markers (i.e., DNA metabarcoding), often requires clustering of DNA sequences into operational taxonomic units (OTUs). Parameters for the clustering procedure tend not to be justified but are set by tradition rather than being based on explicit knowledge. In this study, we explore the extent to which ordination results are affected by variation in parameter settings for the clustering procedure. Amplicon sequence data from nine microbial community studies, representing different sampling designs, spatial scales and ecosystems, were subjected to clustering into OTUs at seven different similarity thresholds (clustering thresholds) ranging from 87% to 99% sequence similarity. The 63 data sets thus obtained were subjected to parallel DCA and GNMDS ordinations. The resulting community structures were highly similar across all clustering thresholds. We explain this pattern by the existence of strong ecological structuring gradients and phylogenetically diverse sets of abundant OTUs that are highly stable across clustering thresholds. Removing low‐abundance, rare OTUs had negligible effects on community patterns. Our results indicate that microbial data sets with a clear gradient structure are highly robust to choice of sequence clustering threshold.     

Distance-based tests for homogeneity of multivariate dispersions

Summary The traditional likelihood‐based test for differences in multivariate dispersions is known to be sensitive to nonnormality. It is also impossible to use when the number of variables exceeds the number of observations. Many biological and ecological data sets have many variables, are highly skewed, and are zero‐inflated. The traditional test and even some more robust alternatives are also unreasonable in many contexts where measures of dispersion based on a non‐Euclidean dissimilarity would be more appropriate. Distance‐based tests of homogeneity of multivariate dispersions, which can be based on any dissimilarity measure of choice, are proposed here. They rely on the rotational invariance of either the multivariate centroid or the spatial median to obtain measures of spread using principal coordinate axes. The tests are straightforward multivariate extensions of Levene's test, with P‐values obtained either using the traditional F‐distribution or using permutation of either least‐squares or LAD residuals. Examples illustrate the utility of the approach, including the analysis of stabilizing selection in sparrows, biodiversity of New Zealand fish assemblages, and the response of Indonesian reef corals to an El Niño. Monte Carlo simulations from the real data sets show that the distance‐based tests are robust and powerful for relevant alternative hypotheses of real differences in spread.     

Multiscale determinants of parasite abundance: A quantitative hierarchical approach for coral reef fishes

During recent decades, there have been numerous attempts to identify the key determinants of parasite communities and several influential variables have been clarified at either infra-, component or compound community scales. However, in view of the possible complexity of interactions among determinants, the commonly-used exploratory and statistical modelling techniques have often failed to find meaningful ecological patterns from such data. Moreover, quantitative assessments of factors structuring species richness, abundance, community structure and species associations in parasite communities remain elusive. Recently, because they are ideally suited for the analysis of complex and highly interactive data, there has been increasing interest in the use of classification and regression tree analyses in several ecological fields. To date, such approaches have never been used by parasitologists for field data. This study aims to both introduce and illustrate the use of multivariate regression trees in order to investigate the determinants of parasite abundance in a multi-scale quantitative context. To do this, we used new field epidemiological data from 1489 coral reef fishes collected around two islands in French Polynesia. We evaluated the relative effect and interactions of several host traits and environmental factors on the abundance of metazoan parasite assemblage at several scales and assessed the impact of major factors on each parasite taxon. Our results suggest that the islands sampled, the host species and host size are equal predictors of parasite abundance at a global scale, whereas other factors proved to be significant predictors of a local pattern, depending on host family. We also discuss the potential use of regression trees for parasitologists as both an explorative and a promising predictive tool.     

The energetic equivalence rule rejected because of a potentially common sampling error: evidence from carabid beetles

Abundance data from pitfall traps are widely used to estimate the relationship between beetle body size and abundance. Such data probably overestimate densities of large bodied species and may overestimate slopes of size‐abundance relationships. Here, we test this idea by comparing size‐abundance patterns found using data from pitfall trapping with those found with data from a quantitative method of estimating abundance, quadrat sampling. We use data from a total of 47 communities. As expected, slopes of size‐abundance relationships are significantly more positive when estimated using data from pitfall traps compared to when using quadrat sampling data. This was seen when looking across different communities, within communities sampled by both methods and when focusing on the set of species found by both methods within a community. These results were also generally found regardless of method of analysis, which were done using regression with species values as independent data points and using the independent contrast method, and with slopes estimated using ordinary least square regression or the structural relation. Most important, slopes of size‐abundance relationships based on data from pitfall traps were on average significantly more positive than ?0.75 on log–log scales, and thus inconsistent with the energetic equivalence rule. Slopes based on quadrat sampling, on the other hand, were on average not significantly different from ?0.75. The rejection of the energetic equivalence rule based on data from pitfall traps here is therefore a sampling artefact. Similar problems may apply to abundance data from virtually all insect trapping methods, and should make us consider re‐examining many of the size‐abundance patterns documented so far. As a large proportion of all animal species are insects, and traps are widely used to estimate abundance, this is a potentially important problem for our general understanding of the relationship between species body size and abundance.     

The relative roles of environment and history as controls of tree species composition and richness in Europe

Aim This study investigates the determinants of European‐scale patterns in tree species composition and richness, addressing the following questions: (1) What is the relative importance of environment and history? History refers to lasting effects of past large‐scale events and time‐dependent cumulative effects of ongoing processes, notably dispersal limited range dynamics. (2) Among the environmental determinants, what is the relative importance of climate, soils, and forest cover? (3) Do the answers to questions 1 and 2 differ between conifers and Fagales, the two major monophyletic groups of European trees? Location The study area comprises most of Europe (34° N–72° N and 11° W–32° E). Methods Atlas data on native distributions of 54 large tree species at 50 × 50 km resolution were linked with climatic, edaphic, and forest cover maps in a geographical information system. Unconstrained (principal components analysis using Hellinger distance transformation and detrended correspondence analysis) and constrained ordinations (redundancy analysis using Hellinger distance transformation and canonical correspondence analysis) and multiple linear regressions were used to investigate the determinants of species composition and species richness, respectively. History is expected to leave its mark as broad spatial patterns and was represented by the nine spatial terms of a cubic trend surface polynomial. Results The main floristic pattern identified by all ordinations was a latitude‐temperature gradient, while the lower axes corresponded mostly to spatial variables. Partitioning the floristic variation using constrained ordinations showed the mixed spatial‐environmental and pure spatial fractions to be much greater than the pure environmental fraction. Biplots, forward variable selection, and partial analyses all suggested climatic variables as more important floristic determinants than forest cover or soil variables. Tree species richness peaked in the mountainous regions of East‐Central and Southern Europe, except the Far West. Variation partitioning of species richness found the mixed spatial‐environmental and pure spatial fractions to be much greater than the pure environmental fraction for all species combined and Fagales, but not for conifers. The scaled regression coefficients indicated climate as a stronger determinant of richness than soils or forest cover. While the dominant patterns were similar for conifers and Fagales, conifers exhibited less predictable patterns overall, a smaller pure spatial variation fraction relative to pure environmental fraction, and a greater relative importance of climate; all differences being more pronounced for species richness than for species composition. Main conclusions The analyses suggest that history is at least as important as current environment in controlling species composition and richness of European trees, with the exception of conifer species richness. Strong support for interpreting the spatial patterns as outcomes of historical processes, notably dispersal limitation, came from the observation that many European tree species naturalize extensively outside their native ranges. Furthermore, it was confirmed that climate predominates among environmental determinants of distribution and diversity patterns at large spatial scales. Finally, the particular patterns exhibited by conifers probably reflect greater environmental specialization and greater human impact. These findings warn against expecting the European tree flora to be able track fast future climate changes on its own.     

Spatial and ontogenetic variability in the microhabitat use of stream-dwelling spined loach (Cobitis taenia) and stone loach (Barbatula barbatula)

Ontogenetic and spatial variability in microhabitat use of spined loach Cobitis taenia (Linnaeus), considered as one species for the purposes of this study, and stone loach Barbatula barbatula (Linnaeus) were examined in the River Great Ouse basin, England, using multivariate and habitat suitability methods, including a technique for handling spatial variation in collections of preference curves. Distinct ordinations of spined age classes and stone loach developmental stages, respectively, in canonical correspondence analysis of species × variables × samples relationships suggest that the two species occupy completely different microhabitats; however, young‐of‐the‐year spined loach occurred more often than expected with all developmental stages of stone loach except young larvae. Water velocity and filamentous algae were the most influential microhabitat variables, the latter decreasing in importance with increasing age of both fish species. Preferred water velocities generally decreased with age in spined loach and increased in stone loach, with substratum size generally increasing with fish age in both species. Spatial variation in microhabitat preferences was great in both species but less so in the spined loach, suggesting that limited plasticity in habitat use could account, at least in part, for the latter species’ limited distribution and abundance in the catchment. Preference curves for a species, if generated and verified for all life intervals and all seasons, could be used as a management tool for a given stream or sector of river basin. But preference curves should be generated for each location to ensure that river management decisions with regard habitat and species conservation consider local‐level species requirements. Thus, a multi‐(eco)species and multi‐scale approach is required in habitat suitability assessments.     

Latitudinal gradients in abundance, and the causes of rarity in the tropics: a test using Australian honeyeaters (Aves: Meliphagidae)

Several studies have uncovered interspecific latitudinal gradients in abundance (population density) such that tropical species tend to be, on average, less abundant than species at higher latitudes. The causes of this relationship remain poorly studied, in contrast to the relative wealth of literature examining the relationship to latitude of other variables such as range size and body mass. We used a cross-species phylogenetic comparative approach and a spatial approach to examine three potential determining factors (distribution, reproductive output and climate) that might explain why abundance correlates with latitude, using data from 54 species of honeyeaters (Meliphagidae) in woodland environments in eastern Australia. There is a strong positive correlation between mean abundance and latitude in these birds. Reproductive output (clutch size) was positively linked to both abundance and latitude, but partial correlation analysis revealed that clutch size is not related to abundance once the effects of latitude are removed. A subsequent multiple regression model that also considered range size, clutch size and body mass showed that latitude is the only strong predictor of abundance in honeyeaters. In the separate spatial analysis, the climatic variables that we considered (temperature, rainfall and seasonality) were all strongly linked to latitude, but none served as a better predictor of abundance than latitude per se, either individually or collectively. The most intriguing result of our analyses was that the cross-species latitudinal pattern in abundance was not evident within species. This suggests an intrinsic cause of the pattern of ‘rarity in the tropics’ in Australian honeyeaters. We suggest that evolutionary age may provide a key to understanding patterns of abundance in these birds.Electronic Supplementary Material Supplementary material is available to authorised users in the online version of this article at .     

Assessing temporal scales and patterns in time series: Comparing methods based on redundancy analysis

Time-series modelling techniques are powerful tools for studying temporal scaling structures and dynamics present in ecological and other complex systems and are gaining popularity for assessing resilience quantitatively. Among other methods, canonical ordinations based on redundancy analysis are increasingly used for determining temporal scaling patterns that are inherent in ecological data. However, modelling outcomes and thus inference about ecological dynamics and resilience may vary depending on the approaches used. In this study, we compare the statistical performance, logical consistency and information content of two approaches: (i) asymmetric eigenvector maps (AEM) that account for linear trends and (ii) symmetric distance-based Moran's eigenvector maps (MEM), which requires detrending of raw data to remove linear trends prior to analysis. Our comparison is done using long-term water quality data (25 years) from three Swedish lakes. This data set therefore provides the opportunity for assessing how the modelling approach used affects performance and inference in time series modelling. We found that AEM models had consistently more explanatory power than MEM, and in two out of three lakes AEM extracted one more temporal scale than MEM. The scale-specific patterns detected by AEM and MEM were uncorrelated. Also individual water quality variables explaining these patterns differed between methods, suggesting that inferences about systems dynamics are dependent on modelling approach. These findings suggest that AEM might be more suitable for assessing dynamics in time series analysis compared to MEM when temporal trends are relevant. The AEM approach is logically consistent with temporal autocorrelation where earlier conditions can influence later conditions but not vice versa. The symmetric MEM approach, which ignores the asymmetric nature of time, might be suitable for addressing specific questions about the importance of correlations in fluctuation patterns where there are no confounding elements of linear trends or a need to assess causality.     

Neo- and archaeophytes respond more strongly than natives to socio-economic mobility and disturbance patterns along an urban–rural gradient

Aims To clarify the distribution and abundance of natives, archaeophytes and neophytes in settlements along an urban–rural gradient we investigated the importance of site‐related and socio‐economic factors within settlements in determining the abundance and species richness of these three groups of plant species. Location The Wetterau region north of Frankfurt/Main, Hesse, Germany. Methods The occurrence and abundance of plant species were recorded in 2003 along transects in 66 settlements in a region north of Frankfurt/Main (Germany). The transect routes had been established in an earlier study in 1974–81. We used ordinations to analyse species composition, fitting 10 independent variables to explain the main gradient in species composition. Additionally, socio‐economic styles (derived from interviews of 1359 households) were fitted, to analyse the influence of these on plant communities. Finally, we analysed species richness by calculating independent contributions of the variables using hierarchical partitioning. Results Many species of neophytes have expanded their range since the 1974–81 survey, but this was not associated with a general decline of archaeophytes and natives. The main variation of species composition was along a rural to urban gradient, associated with site conditions and with socio‐economic factors that vary from settlements dominated by households rooted in the local community to settlements characterized by a mobile new middle class. For neophytes, variables describing the connection to Frankfurt (distance or connectivity) were most important in predicting species richness. For archaeophytes, the data showed a positive relationship between species richness and increasing herbicide application. Main conclusions These results suggest that the occurrence of neophytes within settlements along the urban–rural gradient is triggered by dispersal, related to human mobility patterns. In contrast, the occurrence of archaeophytes in settlements is determined by disturbance‐related factors that predominantly depend on traditional gardening practices and weed control practices associated with farming.     

Relative contribution of neutral and deterministic processes in shaping fruit‐feeding butterfly assemblages in Afrotropical forests

The unified neutral theory of biodiversity and biogeography has gained the status of a quantitative null model for explaining patterns in ecological (meta)communities. The theory assumes that individuals of trophically similar species are functionally equivalent. We empirically evaluate the relative contribution of neutral and deterministic processes in shaping fruit‐feeding butterfly assemblages in three tropical forests in Africa, using both direct (confronting the neutral model with species abundance data) and indirect approaches (testing the predictions of neutral theory using data other than species abundance distributions). Abundance data were obtained by sampling butterflies using banana baited traps set at the forest canopy and understorey strata. Our results indicate a clear consistency in the kind of species or species groups observed at either the canopy or understorey in the three studied communities. Furthermore, we found significant correlation between some flight‐related morphological traits and species abundance at the forest canopy, but not at the understorey. Neutral theory's contribution to explaining our data lies largely in identifying dispersal limitation as a key process regulating fruit‐feeding butterfly community structure. Our study illustrates that using species abundance data alone in evaluating neutral theory can be informative, but is insufficient. Species‐level information such as habitat preference, host plants, geographical distribution, and phylogeny is essential in elucidating the processes that regulate biodiversity community structures and patterns.