The pairwise approach to analysing species co‐occurrence

The analysis of species co‐occurrence patterns continues to be a main pursuit of ecologists, primarily because the coexistence of species is fundamentally important in evaluating various theories, principles and concepts. Examples include community assembly, equilibrium versus non‐equilibrium organization of communities, resource partitioning and ecological character displacement, the local–regional species diversity relationship, and the metacommunity concept. Traditionally, co‐occurrence has been measured and tested at the level of an entire species presence–absence matrix wherein various algorithms are used to randomize matrices and produce statistical null distributions of metrics that quantify structure in the matrix. This approach implicitly recognizes a presence–absence matrix as having some real ecological identity (e.g. a set of species exhibiting nestedness among a set of islands) in addition to being a unit of statistical analysis. An emerging alternative is to test for non‐random co‐occurrence between paired species. The pairwise approach does not analyse matrix‐level structure and thus views a species pair as the fundamental unit of co‐occurrence. Inferring process from pattern is very difficult in analyses of co‐occurrence; however, the pairwise approach may make this task easier by simplifying the analysis and resulting inferences to associations between paired species.     

Multisite and multispecies measures of overlap,co‐occurrence,and co‐diversity

Several indices measure the association or segregation between two species and the similarity or differentiation between two sets of species. These indices are based on the overlap in the distribution of species (measured with the number of co‐occurrences) or on the overlap in species composition of sites (measured with the number of species that are shared between two sites). This paper shows that when evaluating more than two species the number of overlaps and the number of pairwise co‐occurrences are not equal, as it is the case for two species. Equivalently, when comparing more than two species assemblages, the number of overlaps differ from the number of instances of species sharing by pairs of sites (the ‘co‐diversities’). Considering this distinction, two different types of multispecies and multisite indices can be derived: indices of general overlap and indices of co‐occurrence or co‐diversity. Here I present a complete series of the two types of indices that correspond to the popular Jaccard, Sørensen, and Simpson two‐species or two‐site indices. Indices of general overlap are defined by three parameters (the total number of species, the total number of sites, and the total number of occurrences), whereas indices of co‐occurrence or co‐diversity depend on those parameters plus an additional one that is defined by the values of species richness or range size. Consequently, the two types of indices respond differently to null models, depending on the parameters that are fixed or randomized. All indices correlate well with the mean of the traditional indices calculated pair by pair, and the correspondence is extremely close for the new indices of co‐occurrence and co‐diversity. These properties should be useful in clarifying some of the confusion that exists in the current discussion about the advantages and disadvantages of pairwise vs community‐wide approaches in the analysis of diversity.     

Liana co‐occurrence patterns in a temperate rainforest

Questions: Are liana–host interactions structured at the community level? Do liana–host interactions differ between species, growth form guilds or habitats? Location: Otari‐Wilton's Bush, on the southern tip of North Island, New Zealand. The forest contains 75 ha of mature and regenerating conifer–broadleaf forest. Methods: Nine liana species were quantified among 217 trees to test for negative co‐occurrence patterns. We also conducted additional analyses within and among compartments embedded in the community‐level matrix. Liana and host abundance distributions were assessed across two contrasting habitats. Results: Community‐level analyses revealed negative co‐occurrence patterns. Positive, neutral and negative co‐occurrence patterns were found among compartments within the community‐level matrix. Host species compartments were consistent with randomized expectations, while positive co‐occurrence patterns were found within the host species matrix. Negative co‐occurrence patterns were found inconsistently among lianas that share the same region of host space, and those that do not. Conclusions: Overall, results indicate the liana community is structured non‐randomly. Liana–host interactions appear to follow an opportunistic growth strategy and interactions are due mostly to habitat partitioning.     

Co‐occurrence patterns in a diverse arboreal ant community are explained more by competition than habitat requirements

A major goal of community ecology is to identify the patterns of species associations and the processes that shape them. Arboreal ants are extremely diverse and abundant, making them an interesting and valuable group for tackling this issue. Numerous studies have used observational data of species co‐occurrence patterns to infer underlying assembly processes, but the complexity of these communities has resulted in few solid conclusions. This study takes advantage of an observational dataset that is unusually well‐structured with respect to habitat attributes (tree species, tree sizes, and vegetation structure), to disentangle different factors influencing community organization. In particular, this study assesses the potential role of interspecific competition and habitat selection on the distribution patterns of an arboreal ant community by incorporating habitat attributes into the co‐occurrence analyses. These findings are then contrasted against species traits, to explore functional explanations for the identified community patterns. We ran a suite of null models, first accounting only for the species incidence in the community and later incorporating habitat attributes in the null models. We performed analyses with all the species in the community and then with only the most common species using both a matrix‐level approach and a pairwise‐level approach. The co‐occurrence patterns did not differ from randomness in the matrix‐level approach accounting for all ant species in the community. However, a segregated pattern was detected for the most common ant species. Moreover, with the pairwise approach, we found a significant number of negative and positive pairs of species associations. Most of the segregated associations appear to be explained by competitive interactions between species, not habitat affiliations. This was supported by comparisons of species traits for significantly associated pairs. These results suggest that competition is the most important influence on the distribution patterns of arboreal ants within the focal community. Habitat attributes, in contrast, showed no significant influence on the matrix‐wide results and affected only a few associations. In addition, the segregated pairs shared more biological characteristic in common than the aggregated and random ones.     

Species richness correlates of raw and standardized co‐occurrence metrics

Measuring β‐diversity and changes in species composition across multiple sites and environments is a major research focus in macroecology, and a variety of metrics have been proposed to quantify species co‐occurrence patterns in a species × site occurrence matrix. However, indices of β‐diversity and species co‐occurrence are often statistically dependent on the number of species in an assemblage. We compared the results of several common co‐occurrence metrics with patterns generated by a spatially explicit neutral model simulation. We found that all measures of co‐occurrence and β‐diversity, whether raw, rescaled or standardized by a null model expectation, were highly correlated with the total species richness of the landscape. The one important exception were the effect sizes of the fixed–fixed null model algorithm, which preserves row and column sums of the original matrix during matrix randomization. Our results call for a careful interpretation of meta‐analyses of assemblages that differ widely in species richness. At a minimum, observed species richness should be used as a statistical covariate in regression analyses, and results of the fixed–fixed algorithm should be compared carefully with the results of other randomization tests.     

Disentangling community patterns of nestedness and species co-occurrence

Two opposing patterns of meta‐community organization are nestedness and negative species co‐occurrence. Both patterns can be quantified with metrics that are applied to presence‐absence matrices and tested with null model analysis. Previous meta‐analyses have given conflicting results, with the same set of matrices apparently showing high nestedness (Wright et al. 1998) and negative species co‐occurrence (Gotelli and McCabe 2002). We clarified the relationship between nestedness and co‐occurrence by creating random matrices, altering them systematically to increase or decrease the degree of nestedness or co‐occurrence, and then testing the resulting patterns with null models. Species co‐occurrence is related to the degree of nestedness, but the sign of the relationship depends on how the test matrices were created. Low‐fill matrices created by simple, uniform sampling generate negative correlations between nestedness and co‐occurrence: negative species co‐occurrence is associated with disordered matrices. However, high‐fill matrices created by passive sampling generate the opposite pattern: negative species co‐occurrence is associated with highly nested matrices. The patterns depend on which index of species co‐occurrence is used, and they are not symmetric: systematic changes in the co‐occurrence structure of a matrix are only weakly associated with changes in the pattern of nestedness. In all analyses, the fixed‐fixed null model that preserves matrix row and column totals has lower type I and type II error probabilities than an equiprobable null model that relaxes row and column totals. The latter model is part of the popular nestedness temperature calculator, which detects nestedness too frequently in random matrices (type I statistical error). When compared to a valid null model, a matrix with negative species co‐occurrence may be either highly nested or disordered, depending on the biological processes that determine row totals (number of species occurrences) and column totals (number of species per site).     

Co‐occurrence is not evidence of ecological interactions

There is a rich amount of information in co‐occurrence (presence–absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co‐occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co‐occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co‐occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co‐occurrence, along with potential avenues to extract as much information as possible from such data.     

Range‐constrained co‐occurrence simulation reveals little niche partitioning among rock‐dwelling Montenegrina land snails (Gastropoda: Clausiliidae)

Aim

Taxon co‐occurrence analysis is commonly used in ecology, but it has not been applied to range‐wide distribution data of partly allopatric taxa because existing methods cannot differentiate between distribution‐related effects and taxon interactions. Our first aim was to develop a taxon co‐occurrence analysis method that is also capable of taking into account the effect of species ranges and can handle faunistic records from museum databases or biodiversity inventories. Our second aim was to test the independence of taxon co‐occurrences of rock‐dwelling gastropods at different taxonomic levels, with a special focus on the Clausiliidae subfamily Alopiinae, and in particular the genus Montenegrina.

Location

Balkan Peninsula in south‐eastern Europe (46N–36N, 13.5E–28E).

Methods

We introduced a taxon‐specific metric that characterizes the occurrence probability at a given location. This probability was calculated as a distance‐weighted mean of the taxon's presence and absence records at all sites. We applied corrections to account for the biases introduced by varying sampling intensity in our dataset. Then we used probabilistic null‐models to simulate taxon distributions under the null hypothesis of no taxon interactions and calculated pairwise and cumulated co‐occurrences. Independence of taxon occurrences was tested by comparing observed co‐occurrences to simulated values.

Results

We observed significantly fewer co‐occurrences among species and intra‐generic lineages of Montenegrina than expected under the assumption of no taxon interaction.

Main conclusions

Fewer than expected co‐occurrences among species and intra‐generic clades indicate that species divergence preceded niche partitioning. This suggests a primary role of non‐adaptive processes in the speciation of rock‐dwelling gastropods. The method can account for the effects of distributional constraints in range‐wide datasets, making it suitable for testing ecological, biogeographical, or evolutionary hypotheses where interactions of partly allopatric taxa are in question.     

A new integrative framework for large‐scale assessments of biodiversity and community dynamics,using littoral gastropods and crabs of British Columbia,Canada

Improving our understanding of species responses to environmental changes is an important contribution ecologists can make to facilitate effective management decisions. Novel synthetic approaches to assessing biodiversity and ecosystem integrity are needed, ideally including all species living in a community and the dynamics defining their ecological relationships. Here, we present and apply an integrative approach that links high‐throughput, multicharacter taxonomy with community ecology. The overall purpose is to enable the coupling of biodiversity assessments with investigations into the nature of ecological interactions in a community‐level data set. We collected 1195 gastropods and crabs in British Columbia. First, the General mixed Yule‐coalescent (GMYC) and the Poisson Tree Processes (PTP) methods for proposing primary species‐hypotheses based on cox1 sequences were evaluated against an integrative taxonomic framework. We then used data on the geographic distribution of delineated species to test species co‐occurrence patterns for nonrandomness using community‐wide and pairwise approaches. Results showed that PTP generally outperformed GMYC and thus constitutes a more effective option for producing species‐hypotheses in community‐level data sets. Nonrandom species co‐occurrence patterns indicative of ecological relationships or habitat preferences were observed for grazer gastropods, whereas assemblages of carnivorous gastropods and crabs appeared influenced by random processes. Species‐pair associations were consistent with current ecological knowledge, thus suggesting that applying community assembly within a large taxonomical framework constitutes a valuable tool for assessing ecological interactions. Combining phylogenetic, morphological and co‐occurrence data enabled an integrated view of communities, providing both a conceptual and pragmatic framework for biodiversity assessments and investigations into community dynamics.     

The diversity field of New World leaf‐nosed bats (Phyllostomidae)

Aim To analyse how the patterns of species richness for the whole family Phyllostomidae determine the structure of diversity fields (sets of species‐richness values) within the ranges of individual bat species. Location The range of the family Phyllostomidae in North and South America. Methods We generated a database of the occurrence of 143 phyllostomid bat species in 6794 quadrats, analysing the species‐richness frequency distribution for all sites, and for subsets of sites defined by the geographic ranges of species. Range–diversity plots, depicting simultaneously the size and the mean species richness of ranges, were built to explore the patterns of co‐occurrence in widespread and restricted species. We compared the empirical patterns against two null models: (1) with scattered (non‐cohesive) ranges, and (2) with cohesive ranges modelled with the spreading‐dye algorithm. Diversity fields were analysed with richness maps for individual species and with comparisons of species‐richness frequency distributions. Results Overall richness frequency distribution showed a multimodal pattern, whereas simulated distributions showed lower values of variance, and were unimodal (for model 1) and bimodal (for model 2). Range–diversity plots for the empirical data and for the cohesive‐ranges simulation showed a strong tendency of species to co‐occur in high‐diversity sites. The scattered‐ranges simulation showed no such tendency. Diversity fields varied according to idiosyncratic features of species generating particular geographic patterns and richness frequency distributions. Main conclusions Phyllostomid bats show a higher level of co‐occurrence than expected from null models. That tendency in turn implies a higher variance in species richness among sites, generating a wider species‐richness frequency distribution. The diversity field of individual species results from the size, shape and location of ranges, but also depends on the general pattern of richness for the whole family.     

Fine-scale structure and cross-taxon congruence of bird and beetle assemblages in an old-growth boreal forest mosaic

Aim Nestedness occurs when species present in depauperate sites are subsets of those found in species‐rich sites. The degree of congruence of site nestedness among different assemblages can inform commonalities of mechanisms structuring the assemblages. Well‐nested assemblages may still contain idiosyncratic species and sites that notably depart from the typical assemblage pattern. Idiosyncrasy can arise from multiple processes, including interspecific interactions and habitat preferences, which entail different consequences for species co‐occurrences. We investigate the influence of fine‐scale habitat variation on nestedness and idiosyncrasy patterns of beetle and bird assemblages. We examine community‐level and pairwise species co‐occurrence patterns, and highlight the potential influence of interspecific interactions for assemblage structure. Location Côte‐Nord region of Québec, Canada. Methods We sampled occurrences of ground‐dwelling beetles, flying beetles and birds at sites within old‐growth boreal forest. We examined the nestedness and idiosyncrasy of sites and sought relationships to habitat attributes. We analysed non‐random species co‐occurrence patterns at pairwise and community levels, using null model analysis and five ‘association’ indices. Results All three assemblages were significantly nested. There was limited congruence only between birds and flying beetles whose nestedness was related to canopy openness. For ground‐dwelling beetles, nestedness was related to high stand heterogeneity and sapling density, whereas site idiosyncrasy was inversely related to structural heterogeneity. For birds, site idiosyncrasy increased with canopy cover, and most idiosyncratic species were closed‐canopy specialists. In all assemblages, species idiosyncrasy was positively correlated with the frequency of negative pairwise associations. Species co‐occurrence patterns were non‐random, and for flying beetles and birds positive species pairwise associations dominated. Community‐level co‐occurrence summaries may not, however, always reflect these patterns. Main conclusions Nestedness patterns of different assemblages may not correlate, even when sampled at common locations, because of different responses to local habitat attributes. We found idiosyncrasy patterns indicating opposing habitat preferences, consistent with antagonistic interactions among species within assemblages. Analysis of such patterns can thus suggest the mechanisms generating assemblage structures, with implications for biodiversity conservation.     

Using null model analysis of species co-occurrences to deconstruct biodiversity patterns and select indicator species

Aim Using total species richness to characterize biodiversity may mask multiple response patterns of species. We propose a null model analysis of species co‐occurrence‐based classification to identify sets of species that may have similar (within‐groups) and distinct (between groups) response patterns to their environment. The classification should also provide an explicit framework for selecting indicator species with characteristic co‐occurrence patterns to predict overall species richness. Location Côte‐Nord, Québec, Canada. Methods We combined null‐model of species co‐occurrence and cluster analysis to identify species groups within diverse assemblages of ground‐dwelling and flying beetles of stands in a boreal forest mosaic; we then examined their co‐occurrence and response patterns to habitat characteristics. Best subset regressions were used to select indicator species of richness within each group, from which indicators of total species richness were selected. Results The identified species groups appeared to display contrasting co‐occurrence and response patterns to at least one of the stand‐level habitat characteristics. Among flying beetles, for example, richness increased with stand‐level heterogeneity for two groups and decreased for two other groups, but the relationship was non‐significant for the total richness. We identified 28 indicator species that explained > 80% (validated by bootstrap analysis) of the variation in total species richness. Predictive performance of indicators was higher than when their co‐occurrence were reshuffled, even under a highly constrained null model, indicating that co‐occurrence patterns contributed to their predictive performance. Main conclusions Co‐occurrence‐based classification appears as a promising and effective tool for deconstructing biodiversity into species groups which reflect their ecological commonalities and differences, thus reducing the risk of making faulty inferences about the causes underlying overall diversity patterns. The method provides an explicit framework for selecting indicator species representing different species groups that may reflect the multiple responses of species co‐occurring with them. Indicator species can be effective for predicting overall species richness.     

Community‐wide changes in intertaxonomic temporal co‐occurrence resulting from phenological shifts

Global climate change is known to affect the assembly of ecological communities by altering species' spatial distribution patterns, but little is known about how climate change may affect community assembly by changing species' temporal co‐occurrence patterns, which is highly likely given the widely observed phenological shifts associated with climate change. Here, we analyzed a 29‐year phenological data set comprising community‐level information on the timing and span of temporal occurrence in 11 seasonally occurring animal taxon groups from 329 local meteorological observatories across China. We show that widespread shifts in phenology have resulted in community‐wide changes in the temporal overlap between taxa that are dominated by extensions, and that these changes are largely due to taxa's altered span of temporal occurrence rather than the degree of synchrony in phenological shifts. Importantly, our findings also suggest that climate change may have led to less phenological mismatch than generally presumed, and that the context under which to discuss the ecological consequences of phenological shifts should be expanded beyond asynchronous shifts.     

Phylogeny and co‐occurrence of mammal species on Southeast Asian islands

Aim Islands have often been used as model systems in community ecology. The incorporation of information on phylogenetic relatedness of species in studies of island assemblage structure is still uncommon, but could provide valuable insights into the processes of island community assembly. We propose six models of island community assembly that make different predictions about the associations between co‐occurrences of species pairs on islands, phylogenetic relatedness and ecological similarity. We then test these models using data on mammals of Southeast Asian islands. Location Two hundred and forty islands of the Sundaland region of Southeast Asia. Methods We quantified the co‐occurrence of species pairs on islands, and identified pairs that co‐occur more frequently (positive co‐occurrence) or less frequently (negative co‐occurrence) than expected under null models. We then examined the distributions of these significantly deviating pairs with respect to phylogenetic relatedness and ecological differentiation, and compared these patterns with those predicted by the six community assembly models. We used permutation regression to test whether co‐occurrence patterns are predicted by relatedness, body size difference or difference in diet quality. Separate co‐occurrence matrices were analysed in this way for seven mammal families and four smaller subsets of the islands of Sundaland. Results In many matrices, average numbers of negative co‐occurrences were higher than expected under null models. This is consistent with assemblage structuring by competition, but may also result from low geographic overlap of species pairs, which contributes to negative co‐occurrences at the archipelago‐wide level. Distributions of species pairs within plots of phylogenetic distance × ecological differentiation were consistent with competition, habitat filtering or within‐island speciation models, depending on the taxon. Regressions indicated that co‐occurrence was more likely among closely related species pairs within the Viverridae and Sciuridae, but in most matrices phylogenetic distance was unrelated to co‐occurrence. Main conclusions Simple deterministic models linking co‐occurrence with phylogeny and ecology are a useful framework for interpreting distributions and assemblage structure of island species. However, island assemblages in Sundaland have probably been shaped by a complex idiosyncratic set of interacting ecological and evolutionary processes, limiting the predictive power of such models.     

Plant–mycorrhizal fungus co‐occurrence network lacks substantial structure

The interactions between plants and arbuscular mycorrhizal fungi (AMF) maintain a crucial link between macroscopic organisms and the soil microbial world. These interactions are of extreme importance for the diversity of plant communities and ecosystem functioning. Despite this importance, only recently has the structure of plant–AMF interaction networks been studied. These recent studies, which used genetic data, suggest that these networks are highly structured, very similar to plant–animal mutualistic networks. However, the assembly process of plant–AMF communities is still largely unknown, and an important feature of plant–AMF interactions has not been incorporated: they occur at an extremely localized scale. Studying plant–AMF networks in a spatial context seems therefore a crucial step. This paper studies a plant–AMF spatial co‐occurrence network using novel methodology based on information theory and a unique set of spatially explicit species‐level data. We apply three null models of which only one accounts for spatial effects. We find that the data show substantial departures from null expectations for the two non‐spatial null models. However, for the null model considering spatial effects, there are few significant co‐occurrences compared with the other two null models. Thus, plant–AMF spatial co‐occurrences seem to be mostly explained by stochasticity, with a small role for other factors related to plant–AMF specialization. Furthermore, we find that the network is not significantly nested or modular. We conclude that this plant–AMF spatial co‐occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem more important in the first step of the assembly of plant–AMF communities. Synthesis The symbiotic interaction between plants and arbuscular mycorrhizal fungi (AMF) is crucial for ecosystem functioning. However, the factors affecting the assembly of plant‐AMF communities are poorly understood. An important factor of the assembly of plant‐AMF communities has been overlooked: plant‐AMF interactions occur at a localized spatial scale. Our study investigated the importance of space in the structure of plant‐AMF communities. We studied a plant‐AMF spatial co‐occurrence network using a unique set of spatially explicit data and applied three null models. We found that plant‐AMF spatial co‐occurrences seem to be mostly explained by stochasticity. In particular, our study shows that this plant‐AMF spatial co‐occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem to drive the assembly of plant‐AMF communities.     

Disentangling biotic interactions,environmental filters,and dispersal limitation as drivers of species co‐occurrence

A key focus in ecology is to search for community assembly rules. Here we compare two community modelling frameworks that integrate a combination of environmental and spatial data to identify positive and negative species associations from presence–absence matrices, and incorporate an additional comparison using joint species distribution models (JSDM). The frameworks use a dichotomous logic tree that distinguishes dispersal limitation, environmental requirements, and interspecific interactions as causes of segregated or aggregated species pairs. The first framework is based on a classical null model analysis complemented by tests of spatial arrangement and environmental characteristics of the sites occupied by the members of each species pair (Classic framework). The second framework, (SDM framework) implemented here for the first time, builds on the application of environmentally‐constrained null models (or JSDMs) to partial out the influence of the environment, and includes an analysis of the geographical configuration of species ranges to account for dispersal effects. We applied these approaches to examine plot‐level species co‐occurrence in plant communities sampled along a wide elevation gradient in the Swiss Alps. According to the frameworks, the majority of species pairs were randomly associated, and most of the non‐random positive and negative species associations could be attributed to environmental filtering and/or dispersal limitation. These patterns were partly detected also with JSDM. Biotic interactions were detected more frequently in the SDM framework, and by JSDM, than in the Classic framework. All approaches detected species aggregation more often than segregation, perhaps reflecting the important role of facilitation in stressful high‐elevation environments. Differences between the frameworks may reflect the explicit incorporation of elevational segregation in the SDM framework and the sensitivity of JSDM to the environmental data. Nevertheless, all methods have the potential to reveal general patterns of species co‐occurrence for different taxa, spatial scales, and environmental conditions.     

Using biodiversity deconstruction to disentangle assembly and diversity dynamics of understorey plants along post‐fire succession in boreal forest

Aim The study aims to decipher the co‐occurrence of understorey plant assemblages and, accordingly, to identify a set of species groups (diversity deconstruction) to better understand the multiple causal processes underlying post‐fire succession and diversity patterns in boreal forest. Location North‐eastern Canadian boreal forest (49°07′–51°44′ N; 70°13′–65°15′ W). Methods Data on understorey plant communities and habitat factors were collected from 1097 plots. Species co‐occurrence was analysed using null model analysis. We derive species groups (i.e. biodiversity deconstruction) using the strength of pairwise species co‐occurrences after accounting for random expectation under a null model and cluster analyses. We examine the influence of a set of spatiotemporal environmental variables (overstorey composition, time‐since‐fire, spatial location and topography) on richness of species groups using Bayesian model averaging, and their relative influence through hierarchical partitioning of variance. Results Understorey plant assemblages were highly structured, with co‐occurrence‐based classification providing species groups that were coherently aggregated within, but variably segregated between, species groups. Group richness models indicate both common and distinct responses to factors affecting plant succession. For example, Group 2 (e.g. Rhododendron groenlandicum and Cladina rangiferina) showed concurrent contrasting responses to overstorey composition and was strongly segregated from Groups 3 (e.g. Clintonia borealis and Maianthenum canadense) and 4 (e.g. Epilobium angustifolium and Alnus rugosa). Groups 3 and 4 showed partial similarity, but they differed in their response to time‐since‐fire, drainage and latitude, which were more important for Group 1 (e.g. Ptilium crista‐castrensis and Empetrum nigrum). A single successional model based on total richness masked crucial group‐level relationships with factors that we examined, such as latitude. Main conclusions By demonstrating the co‐occurrence structure and linking to causal factors, the results from this study characterize both common and distinct responses of understorey plants to biophysical attributes of sites, and potential interspecific interactions, behind non‐random assemblage structure during post‐fire succession. A biodiversity deconstruction approach could offer a concise and explicit framework to gain a better understanding of the complex assembly of ecological communities during succession.     

Fruit infestation patterns by Anastrepha fraterculus and Ceratitis capitata reveal that cross‐recognition does not lead to complete avoidance of interspecific competition in nature

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Shared environmental responses drive co‐occurrence patterns in river bird communities

Positive or negative patterns of co‐occurrence might imply an influence of biotic interactions on community structure. However, species may co‐occur simply because of shared environmental responses. Here, we apply two complementary modelling methodologies – a probabilistic model of significant pairwise associations and a hierarchical multivariate probit regression model – to 1) attribute co‐occurrence patterns in 100 river bird communities to either shared environmental responses or to other ecological mechanisms such as interaction with heterospecifics, and 2) examine the strength of evidence for four alternative models of community structure. Species co‐occurred more often than would be expected by random community assembly and the species composition of bird communities was highly structured. Co‐occurrence patterns were primarily explained by shared environmental responses; species’ responses to the environmental variables were highly divergent, with both strong positive and negative environmental correlations occurring. We found limited evidence for behaviour‐driven assemblage patterns in bird communities at a large spatial scale, although statistically significant positive associations amongst some species suggested the operation of facilitative mechanisms such as heterospecific attraction. This lends support to an environmental filtering model of community assembly as being the principle mechanism shaping river bird community structure. Consequently, species interactions may be reduced to an ancillary role in some avifaunal communities, meaning if shared environmental responses are not quantified studies of co‐occurrence may overestimate the role of species interactions in shaping community structure.     

Soil environmental heterogeneity allows spatial co‐occurrence of competitor earthworm species in a gallery forest of the Colombian ‘Llanos’

Disentangling how communities of soil organisms are deterministically structured by abiotic and biotic factors is of utmost relevance, and few data sets on co‐occurrence patterns exist in soil ecology compared to other disciplines. In this study, we assessed species spatial co‐occurrence and niche overlap together with the heterogeneity of selected soil properties in a gallery forest (GF) of the Colombian Llanos. We used null‐model analysis to test for non‐random patterns of species co‐occurrence and body size in assemblages of earthworms and whether the pattern observed was the result of environmental heterogeneity or biotic processes structuring the community at small scales by means of co‐inertia analysis (CoIA). The results showed that earthworm species co‐occurred more frequently than expected by chance at short distances, and CoIA highlighted a significant specific relationship between earthworm species and soil variables. The effect of soil environmental heterogeneity on one litter‐feeding species but also the impact of soil‐feeding species on soil physical properties was revealed. Correlogram analysis on the first axis extracted in the CoIA showed the scale of the common structure shared by the fauna and soil variable tables. The earthworm community was not deterministically structured by competition and co‐occurrence of competing species was facilitated by soil environmental heterogeneity at small scales in the GF. Our results agreed with the coexistence aggregation model which suggests that spatial aggregation of competitors at patchily distributed resources (environment) can facilitate species coexistence.