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.     

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.     

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.     

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.     

Null model analysis of communities on gradients

Aim I employed a novel null model and metric to uncover unusual species co‐occurrence patterns in a herpetofaunal assemblage of 49 species collected at discrete elevations along a gradient. Location Mount Kupe, Cameroon. Methods Using a construction algorithm that started from a matrix of 0s, a sample null space of 25,000 unique null matrices was generated by simultaneously conserving (1) the number of occurrences of each species, (2) site richness and (3) species range spans derived from the observed incidence matrix. I then compared the number of times each pair of confamilial species co‐occurred in the null space with the same number derived from the observed incidence matrix. Two cases dealing with embedded absences in species ranges were tested: (1) embedded absences were maintained, and (2) embedded absences were assumed to be sampling omissions and were replaced by presences. Results In the observed absence/presence assemblage there were 147 possible confamilial species pairs. Therefore, 5% or eight were expected by chance alone to have co‐occurrence patterns that differed from chance expectations by chance alone. Of these confamilial species pairs, 38 were congeneric and so 5% or two were expected to differ from chance expectations. For case (1) 16, and for case (2) 17 confamilial species pairs’ co‐occurrence patterns differed significantly from chance expectations. For case (1) nine congeneric species pairs, and for case (2) 10 congeneric pairs differed significantly from chance expectations. For case (1) four, and for case (2) five congeneric species pairs formed checkerboards (patterns of mutual exclusion). Results from case (1) were a proper subset of case (2) indicating that sampling omissions did not alter greatly the results. Main conclusions I have demonstrated that null models are valuable tools to analyse ecological communities provided that proper models are employed. The choice of the appropriate null space to analyse distributions is critical. The null model employed to analyse birds on islands of an archipelago can be adapted to analyse species along gradients provided an additional range constraint is added to the null model. Moreover, added precision to results can be obtained by analysing each species pair separately, particularly those in the same family or genus, as opposed to applying a community‐wide metric to the faunal assemblage. My results support some of the speculations of previous authors who were unable to demonstrate their suspicions analytically.     

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.     

Can environmental constraints determine random patterns of plant species co-occurrence?

Plant community ecologists use the null model approach to infer assembly processes from observed patterns of species co‐occurrence. In about a third of published studies, the null hypothesis of random assembly cannot be rejected. When this occurs, plant ecologists interpret that the observed random pattern is not environmentally constrained – but probably generated by stochastic processes. The null model approach (using the C‐score and the discrepancy index) was used to test for random assembly under two simulation algorithms. Logistic regression, distance‐based redundancy analysis, and constrained ordination were used to test for environmental determinism (species segregation along environmental gradients or turnover and species aggregation). This article introduces an environmentally determined community of alpine hydrophytes that presents itself as randomly assembled. The pathway through which the random pattern arises in this community is suggested to be as follows: Two simultaneous environmental processes, one leading to species aggregation and the other leading to species segregation, concurrently generate the observed pattern, which results to be neither aggregated nor segregated – but random. A simulation study supports this suggestion. Although apparently simple, the null model approach seems to assume that a single ecological factor prevails or that if several factors decisively influence the community, then they all exert their influence in the same direction, generating either aggregation or segregation. As these assumptions are unlikely to hold in most cases and assembly processes cannot be inferred from random patterns, we would like to propose plant ecologists to investigate specifically the ecological processes responsible for observed random patterns, instead of trying to infer processes from patterns.     

An improved method for the identification of areas of endemism using species co-occurrences

Aim To develop a simple method that (1) combines the notions of biotic elements (groups of taxa with ranges significantly more similar to each other than to the ranges of other taxa) and of areas of endemism (AoE, areas of non‐random distributional congruence among taxa), and (2) overcomes the constraints of a previously suggested null model‐based method that cannot deal with disjunctions and is strictly grid‐dependent. Location We used test data sets from southern Africa and Crete. Methods First, we used a null‐model approach to detect pairs of species that have a significant degree of co‐occurrence, in order to determine biotic elements. Subsequently, we used a parsimony analysis of endemicity to delineate candidate AoE, and multivariate analysis to define groups of biotic elements on the basis of species interactions (co‐occurrence, mutual exclusion, neutral) using only the species detected in the previous step. We applied this method to the well known data set for Sciobius in southern Africa, as well as to endemic invertebrates of Crete (Greece), in order to evaluate its performance. Results Our results are very similar to those of previous analyses, and produce meaningful delineation of AoE and biotic elements in both data sets. The method is flexible regarding null models and significance levels, and eliminates noise in the data. Main conclusions We offer a simple method that provides reasonable identification of both biotic elements and AoE, produces good‐fit statistics, reduces uninformative or junk output, and reduces computational time.     

An integrated framework to improve the concept of resource specialisation

Resource specialisation, although a fundamental component of ecological theory, is employed in disparate ways. Most definitions derive from simple counts of resource species. We build on recent advances in ecophylogenetics and null model analysis to propose a concept of specialisation that comprises affinities among resources as well as their co‐occurrence with consumers. In the distance‐based specialisation index (DSI), specialisation is measured as relatedness (phylogenetic or otherwise) of resources, scaled by the null expectation of random use of locally available resources. Thus, specialists use significantly clustered sets of resources, whereas generalists use over‐dispersed resources. Intermediate species are classed as indiscriminate consumers. The effectiveness of this approach was assessed with differentially restricted null models, applied to a data set of 168 herbivorous insect species and their hosts. Incorporation of plant relatedness and relative abundance greatly improved specialisation measures compared to taxon counts or simpler null models, which overestimate the fraction of specialists, a problem compounded by insufficient sampling effort. This framework disambiguates the concept of specialisation with an explicit measure applicable to any mode of affinity among resource classes, and is also linked to ecological and evolutionary processes. This will enable a more rigorous deployment of ecological specialisation in empirical and theoretical studies.     

Pattern detection in null model analysis

Synthesis The identification of distinctive patterns in species x site presence‐absence matrices is important for understanding meta‐community organisation. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re‐ordered to highlight aggregated pairs, indicating that these seemingly opposite patterns are closely related. Recently proposed classification schemes failed to correctly classify realistic matrices that included multiple co‐occurrence structures. We propose using a combination of metrics and decomposing matrix‐wide patterns into those of individual pairs of species and sites to pinpoint sources of non‐randomness. Null model analysis has been a popular tool for detecting pattern in binary presence–absence matrices, and previous tests have identified algorithms and metrics that have good statistical properties. However, the behavior of different metrics is often correlated, making it difficult to distinguish different patterns. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re‐ordered to highlight aggregated pairs. As a consequence, the same null model can identify a single matrix as being simultaneously aggregated, segregated or nested. These results cast doubt on previous conclusions of matrix‐wide species segregation based on the C‐score and the fixed‐fixed algorithm. Similarly, we found that recently proposed classification schemes based on patterns of coherence, nestedness, and segregation and aggregation cannot be uniquely distinguished using proposed metrics and null model algorithms. It may be necessary to use a combination of different metrics and to decompose matrix‐wide patterns into those of individual pairs of species or pairs of sites to pinpoint the sources of non‐randomness.     

Persistent Controversy in Statistical Approaches in Wildlife Sciences: A Perspective of Students

ABSTRACT The controversy over the use of null hypothesis statistical testing (NHST) has persisted for decades, yet NHST remains the most widely used statistical approach in wildlife sciences and ecology. A disconnect exists between those opposing NHST and many wildlife scientists and ecologists who conduct and publish research. This disconnect causes confusion and frustration on the part of students. We, as students, offer our perspective on how this issue may be addressed. Our objective is to encourage academic institutions and advisors of undergraduate and graduate students to introduce students to various statistical approaches so we can make well-informed decisions on the appropriate use of statistical tools in wildlife and ecological research projects. We propose an academic course that introduces students to various statistical approaches (e.g., Bayesian, frequentist, Fisherian, information theory) to build a foundation for critical thinking in applying statistics. We encourage academic advisors to become familiar with the statistical approaches available to wildlife scientists and ecologists and thus decrease bias towards one approach. Null hypothesis statistical testing is likely to persist as the most common statistical analysis tool in wildlife science until academic institutions and student advisors change their approach and emphasize a wider range of statistical methods.     

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.     

Nonrandom co-occurrence patterns of rainforest chameleons

Null‐model analysis of co‐occurrence patterns is a powerful tool to identify ‘structure’ in community ecology data sets. We evaluated the community structure of chameleons in rainforest regions of Nigeria and Cameroon using available data in the literature, including peer‐reviewed articles and unpublished environmental reports to industries. We performed Monte Carlo simulations (5000 iterations, using the sequential swap algorithm) under several model assumptions to derive co‐occurrence patterns among species. Food and spatial (habitat) segregation patterns in both lowland rainforest and montane forest were investigated. We subjected four indices of co‐occurrence patterns (C‐ratio, number of checkerboard species pairs, number of species combinations, and V‐score) to randomization procedures. Overall, the chameleon communities do not show random organization, but instead exhibit precise deterministic patterns. In lowland rainforest, chameleon communities are assembled deterministically along the food niche resource axis, but not along the habitat niche resource axis. The opposite holds for chameleon communities in montane rainforest. We predict that these patterns can be generalized to other regions of tropical Africa, thus helping to determine the general structure of chameleon communities in tropical African forests.     

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.     

Random sampling does not exclude spatial dependence: The importance of neutral models for ecological hypothesis testing

Lájer (2007) notes that, to investigate phytosociological and ecological relationships, many authors apply traditional inferential tests to sets of relevés obtained by non-random methods. Unfortunately, this procedure does not provide reliable support for hypothesis testing because non-random sampling violates the assumptions of independence required by many parametric inferential tests. Instead, a random sampling scheme is recommended. Nonetheless, random sampling will not eliminate spatial autocorrelation. For instance, a classical law of geography holds that everything in a piece of (biotic) space is interrelated, but near objects are more related than distant ones. Because most ecological processes that shape community structure and species coexistence are spatially explicit, spatial autocorrelation is a vital part of almost all ecological data. This means that, independently from the underlying sampling design, ecological data are generally spatially autocorrelated, violating the assumption of independence that is generally required by traditional inferential tests. To overcome this drawback, randomization tests may be used. Such tests evaluate statistical significance based on empirical distributions generated from the sample and do not necessarily require data independence. However, as concerns hypothesis testing, randomization tests are not the universal remedy for ecologists, because the choice of inadequate null models can have significant effects on the ecological hypotheses tested. In this paper, I emphasize the need of developing null models for which the statistical assumptions match the underlying biological mechanisms.     

A dimmer shade of pale: revealing the faint signature of local assembly processes on the structure of strongly filtered plant communities

Trait‐based ecology suggests that abiotic filtering is the main mechanism structuring the regional species pool in different subsets of habitat‐specific species. At more local spatial scales, other ecological processes may add on giving rise to complex patterns of functional diversity (FD). Understanding how assembly processes operating on the habitat‐specific species pools produce the locally observed plant assemblages is an ongoing challenge. Here, we evaluated the importance of different processes to community assembly in an alpine fellfield, assessing its effects on local plant trait FD. Using classical randomization tests and linear mixed models, we compared the observed FD with expectations from three null models that hierarchically incorporate additional assembly constraints: stochastic null models (random assembly), independence null models (each species responding individual and independently to abiotic environment), and co‐occurrence null models (species responding to environmental variation and to the presence of other species). We sampled species composition in 115 quadrats across 24 locations in the central Pyrenees (Spain) that differed in soil conditions, solar radiation and elevation. Overall, the classical randomization tests were unable to find differences between the observed and expected functional patterns, suggesting that the strong abiotic filters that sort out the flora of extreme regional environments blur any signal of other local processes. However, our approach based on linear mixed models revealed the signature of different ecological processes. In the case of seed mass and leaf thickness, observed FD significantly deviated from the expectations of the stochastic model, suggesting that fine‐scale abiotic filtering and facilitation can be behind these patterns. Our study highlights how the hierarchical incorporation of ecological additional constraints may shed light on the dim signal left by local assembly processes in alpine environments.     

Testing the link between species interactions and species co‐occurrence in a trophic network

Species interactions are dynamic processes that vary across environmental and ecological contexts, and operate across scale boundaries, making them difficult to quantify. Nevertheless, ecologists are increasingly interested in inferring species interactions from observational data using statistical analyses of their spatial co‐occurrence patterns. Trophic interactions present a particular challenge, as predators and prey may frequently or rarely co‐occur, depending on the spatial or temporal scale of observation. In this study, we investigate the accuracy of inferred interactions among species that both compete and trophically interact. We utilized a long‐term dataset of pond‐breeding amphibian co‐occurrences from Mt Rainier National Park (Washington, USA) and compiled a new dataset of their empirical interactions from the literature. We compared the accuracy of four statistical methods in inferring these known species interactions from spatial associations. We then used the best performing statistical method, the Markov network, to further investigate the sensitivity of interaction inference to spatial scale‐dependence and the presence of predators. We show that co‐occurrence methods are generally inaccurate when estimating trophic interactions. Further the strength and sign of inferred interactions were dependent upon the spatial scale of observation and predator presence influenced the detectability of competitive interactions among prey species. However, co‐occurrence analysis revealed new patterns of spatial association among pairs of species with known interactions. Overall, our study highlights a limiting frontier in co‐occurrence theory and the disconnect between widely implemented methodologies and their ability to accurately infer interactions in trophically‐structured communities.